CN105159093B - Microgrid energy Optimal Control System and its design method based on model adaptation - Google Patents
Microgrid energy Optimal Control System and its design method based on model adaptation Download PDFInfo
- Publication number
- CN105159093B CN105159093B CN201510643423.XA CN201510643423A CN105159093B CN 105159093 B CN105159093 B CN 105159093B CN 201510643423 A CN201510643423 A CN 201510643423A CN 105159093 B CN105159093 B CN 105159093B
- Authority
- CN
- China
- Prior art keywords
- energy
- data
- micro
- model
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of microgrid energy Optimal Control System based on model adaptation, including what is be linked in sequence：Mechanical floor, model layer, calculation layer, service layer and application layer this 5 logical levels；The invention also discloses a kind of design methods of the microgrid energy Optimal Control System based on model adaptation：Including being based on real-time monitoring system acquisition electric network information, each distributed electrical source information and information on load etc., by carrying out device class, model foundation, calculation optimization, energy source configuration response and application monitoring in real time to the information acquired.Development cost and period present invention reduces Energy Management System entirety, the optimal power matching between power grid, each distributed generation resource, energy storage and load is realized, while is realized to each distributed generation resource in isolated island and flexible control during grid-connected two kinds of operational modes and system.
Description
Technical field
The present invention relates to a kind of microgrid energy Optimal Control Systems based on model adaptation, further relate to a kind of based on mould
The design method of the adaptive microgrid energy Optimal Control System of type, belongs to micro-capacitance sensor technical field.
Background technology
New energy micro-capacitance sensor is mutual based on all kinds of distributed energy multipotencys such as localized power distribution net construction, wind, light, natural gas
It mends, has more high-new electricity power access ratio, by energy stores and realization indigenous energy production can be distributed rationally with using energy
Load is in a basic balance, can as needed with public electric wire net flexible interaction and the intelligent comprehensive utilization of energy office of relatively independent operation
Domain net.New energy micro-capacitance sensor can be multiple new energy micro-capacitance sensor structures in single new energy micro-capacitance sensor or a certain region
Into micro-capacitance sensor group.Micro-capacitance sensor uses advanced internet and information technology, the intelligent matching realized production of energy and used
And synthetic operation, form the using energy source new support of high-efficiency cleaning.
At present, mainly include for the research of microgrid energy management system：It is run using monitoring systematic steady state is laid particular emphasis on
The SCADA of situation（Supervisory Control And Data Acquisition）Information collection and monitoring are carried out, to each
Distributed generation resource carries out real time implementation, the running optimizatin control of multiple target；Based on comprising mechanical floor, management level and optimization layer these three
The layered protection strategy and Information Interaction Model of logical level realize microgrid energy management；Based on central controller hierarchical control
Microgrid energy management strategy, analysis micro-capacitance sensor operation two kinds of marketing policys；Islet operation energy based on time domain layering
Management strategy, it is pre- which according to the difference of time domain by microgrid energy management is divided into the short-term power generation power based on meteorological condition
Survey and the advanced energy management of load prediction, taken over seamlessly based on operational mode energy management, based on power model- following control
Real-time power manage this 3 layers, and the energy pipe of whole system is realized by the coordinated operation between this 3 layers of energy managements
Reason.
Traditional microgrid energy management system, though the algorithm structure design with perfect information acquisition system, optimization,
But the data mining processing to information is ignored, limits apparatus function extension, can not realize the overall dynamics point to micro-capacitance sensor
Analysis, Simultaneous Monitoring, it is difficult to carry out fine granularity customizing functions and non-intrusion type to system according to the type, structure and scale of micro-capacitance sensor
Extension.
Invention content
It is an object of the invention to overcome deficiency of the prior art, a kind of micro-capacitance sensor energy based on model adaptation is provided
Optimal Control System is measured, solution has micro-capacitance sensor type, structure, the method for operation are flexible and changeable to lead to microgrid energy in the prior art
The problem of management system can not be adaptive.
In order to solve the above technical problems, the microgrid energy optimal control system provided by the invention based on model adaptation
System, the logical level being linked in sequence including 5：Mechanical floor, model layer, calculation layer, service layer and application layer；
The mechanical floor is connect with real-time monitoring system signal, for the micro-capacitance sensor data that are acquired to real-time monitoring system into
Row device class；
The model layer establishes micro-grid system model using the module data and each model characteristics of mechanical floor；
The calculation layer calculates to micro-grid system model optimization, formulates optimal energy management strategy, and is based on going through
History and real time data determine control instruction；
The service layer realizes power cost most in micro-capacitance sensor operational process, by performing corresponding demand responsive measures
Smallization, carbon emission are cut down；
Application layer：The micro-grid system model for optimizing operation is evaluated.
The mechanical floor includes：Electricity generation module, energy-storage module, loading module and power grid module.
Another object of the present invention is to provide a kind of microgrid energy Optimal Control System based on model adaptation
Design method includes the following steps：
Step 1：Micro-grid system data are obtained by real-time monitoring system；
Step 2：The module data of mechanical floor is carried out matching with micro-grid system data to be associated with, each module data and work(
Energy service corresponds, and data exchange is realized by corresponding function services；
Step 3：Model layer is defined by 4 classifications according to micro-grid system type：Renewable energy power generation/non-renewable
Energy power generation, circular form/non-circulation type, factory/building/house, be incorporated into the power networks/islet operation；
Step 4：Using each model characteristics of model defined analysis of the module data and model layer of mechanical floor, micro- electricity is established
Net system model realizes the data self-described of object-oriented；
Step 5：By microclimate monitoring assembly, the microclimate information of acquisition micro-capacitance sensor locality, and establish corresponding history
Database；
Step 6：Power generation characteristics analysis is carried out to each distributed generation resource, prediction scheme is designed based on historical data base, is carried out
Generated power forecasting, Load Characteristic Analysis and load prediction, and to micro-grid system model carry out Energy Efficiency Analysis, Load flow calculation with
State estimation；
Step 7：According to optimization aim, optimal energy management strategy is formulated, and based on history and in real time by the mathematics plan law
Data determine control instruction；
Step 8：In micro-capacitance sensor operational process, realize that power cost is minimum by performing corresponding demand responsive measures
Change, carbon emission is cut down；
Step 9：The micro-grid system for optimizing operation is evaluated.
Micro-grid system data described in step 1 includes electric network data, each distributed electrical source data, energy storage data and bears
Lotus data；Correspondingly, the mechanical floor includes electricity generation module, energy-storage module, meets module and power grid module.
Microclimate information described in step 5 includes：Temperature, humidity, wind speed, wind direction and solar irradiance.
Optimization aim described in step 7 refers to：Comprehensive benefit is optimal, regenerative resource maximally utilizes, energy consumption with
System loss minimum, cost of electricity-generating minimum, working life longest, power supply reliability highest.
Evaluation described in step 9 includes：Power quality evaluation, the evaluation of system safety, energy-optimised scheduling, electricity market
Transaction and carbon emission evaluation.
Compared with prior art, the advantageous effect that is reached of the present invention is：
1st, based on real-time monitoring system acquisition electric network information, each distributed electrical source information and information on load etc., by institute
The information of acquisition carries out device class, model foundation, calculation optimization, energy source configuration response and application monitoring in real time, shortens energy
The development cost of buret reason system entirety and period, realize the optimal work(between power grid, each distributed generation resource, energy storage and load
Rate matches, while realizes to each distributed generation resource flexibly controlling in isolated island and grid-connected two kinds of operational modes and the optimization of system
Operation；
2nd, while having the algorithm structure design of perfect information acquisition system, optimization, to micro-grid system information
Data mining processing, Simultaneous Monitoring and the overall dynamics analysis of depth refinement are carried out, passes through fine-grained Custom modules and function
Extension actively adapts to the flexible and changeable of micro-grid operation mode and system structure, realizes the flexible control to system and optimizes and transports
Row.
The present invention contributes to the energy management of micro-grid system with optimization operation study the safe and stable operation of micro-capacitance sensor
And different running method studies the influence that microgrid energy management controls.
Description of the drawings
Fig. 1 is the structure diagram of the microgrid energy Optimal Control System based on model adaptation.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and be not intended to limit the protection scope of the present invention and limit the scope of the invention.
As shown in Figure 1, it is the structure diagram of the microgrid energy Optimal Control System based on model adaptation, including 5
The logical level being linked in sequence：Mechanical floor, model layer, calculation layer, service layer and application layer.
Mechanical floor is connect with real-time monitoring system signal, and real-time monitoring system is used to obtain micro-grid system data, including：
Electric network data, each distributed electrical source data, energy storage data and load data.Correspondingly, mechanical floor includes：Electricity generation module, energy storage
Module, loading module and power grid module, each module are associated with micro-grid system data match.
Model layer is defined as by 4 classifications according to micro-grid system type：Renewable energy power generation/non-regeneration energy hair
Electricity, circular form/non-circulation type, factory/building/house, be incorporated into the power networks/islet operation.
Calculation layer includes：Electric power visualization, power generation prediction, load prediction, optimization computation and equipment control.Layer is calculated to use
In the optimization that calculates to micro-grid system model, optimal energy management strategy is formulated, and determine based on history and real time data
Control instruction.
Service layer responds for energy source configuration, in micro-capacitance sensor operational process, by performing corresponding demand responsive measures
Realize that power cost minimizes, carbon emission is cut down.
Application layer：The micro-grid system model for optimizing operation is evaluated.
The design method of microgrid energy Optimal Control System based on model adaptation, includes the following steps：
Step 1：Micro-grid system data are obtained by real-time monitoring system, including electric network data, each distributed generation resource number
According to, energy storage data and load data etc.；
Step 2：Utilize system of electricity generation module, energy-storage module, loading module and the power grid module in mechanical floor with obtaining
Data carry out matching association, and each mould data in the block are corresponded with function services, and data exchange passes through corresponding function services
It realizes；
Step 3：Model layer is defined as by 4 classifications according to micro-grid system type：Renewable energy power generation/non-can be again
Raw energy power generation, circular form/non-circulation type, factory/building/house, be incorporated into the power networks/islet operation；
Step 4：Using each model characteristics of model defined analysis of the module data and model layer of mechanical floor, micro- electricity is established
Net system model realizes the data self-described of object-oriented；
Step 5：By microclimate monitoring assembly, acquisition micro-capacitance sensor local temperature, humidity, wind speed, wind direction and solar irradiation
The microclimates information such as degree, and establish corresponding historical data base；
Step 6：Power generation characteristics analysis is carried out to each distributed generation resource, prediction scheme is designed based on historical data base, is carried out
Generated power forecasting, Load Characteristic Analysis and load prediction, and to micro-grid system model carry out Energy Efficiency Analysis, Load flow calculation with
State estimation；
Step 7：According to comprehensive benefit is optimal, regenerative resource maximally utilizes, energy consumption and system loss are minimum,
Cost of electricity-generating minimum, working life longest, the most high optimization aim of power supply reliability formulate optimal energy pipe by the mathematics plan law
Reason strategy, and control instruction is determined based on history and real time data；
Step 8：In micro-capacitance sensor operational process, realize that power cost is minimum by performing corresponding demand responsive measures
Change, carbon emission reduction etc.；
Step 9：Power quality evaluation, system is carried out to the micro-grid system for optimizing operation to evaluate safely, is energy-optimised
Scheduling, power market transaction and carbon emission evaluation.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformation can also be made, these are improved and deformation
Also it should be regarded as protection scope of the present invention.
Claims (5)
1. the design method of the microgrid energy Optimal Control System based on model adaptation, which is characterized in that including walking as follows
Suddenly：
Step 1：Micro-grid system data are obtained by real-time monitoring system；
Step 2：The module data of mechanical floor is carried out matching with micro-grid system data to be associated with, each module data takes with function
Business corresponds, and data exchange is realized by corresponding function services；
Step 3：Model layer is defined by 4 classifications according to micro-grid system type：Renewable energy power generation/non-regeneration energy
Power generation, circular form/non-circulation type, factory/building/house, be incorporated into the power networks/islet operation；
Step 4：Using each model characteristics of model defined analysis of the module data and model layer of mechanical floor, micro-capacitance sensor system is established
System model, realizes the data self-described of object-oriented；
Step 5：By microclimate monitoring assembly, the microclimate information of acquisition micro-capacitance sensor locality, and establish corresponding historical data
Library；
Step 6：Power generation characteristics analysis is carried out to each distributed generation resource, prediction scheme is designed based on historical data base, is generated electricity
Power prediction, Load Characteristic Analysis and load prediction, and Energy Efficiency Analysis, Load flow calculation and state are carried out to micro-grid system model
Estimation；
Step 7：According to optimization aim, optimal energy management strategy is formulated, and based on history and real time data by the mathematics plan law
Determine control instruction；
Step 8：In micro-capacitance sensor operational process, power cost minimum, carbon are realized by performing corresponding demand responsive measures
Emission reduction；
Step 9：The micro-grid system for optimizing operation is evaluated.
2. the design method of the microgrid energy Optimal Control System according to claim 1 based on model adaptation,
It is characterized in that, micro-grid system data described in step 1 includes electric network data, each distributed electrical source data, energy storage data and bears
Lotus data；Correspondingly, the mechanical floor includes electricity generation module, energy-storage module, loading module and power grid module.
3. the design method of the microgrid energy Optimal Control System according to claim 1 based on model adaptation,
It is characterized in that, microclimate information described in step 5 includes：Temperature, humidity, wind speed, wind direction and solar irradiance.
4. the design method of the microgrid energy Optimal Control System according to claim 1 based on model adaptation,
It is characterized in that, optimization aim described in step 7 refers to：Comprehensive benefit is optimal, regenerative resource maximally utilizes, energy consumption
With system loss minimum, cost of electricity-generating minimum, working life longest, power supply reliability highest.
5. the design method of the microgrid energy Optimal Control System according to claim 1 based on model adaptation,
It is characterized in that, evaluation described in step 9 includes：Power quality evaluation, the evaluation of system safety, energy-optimised scheduling, electricity market
Transaction and carbon emission evaluation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510643423.XA CN105159093B (en) | 2015-10-08 | 2015-10-08 | Microgrid energy Optimal Control System and its design method based on model adaptation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510643423.XA CN105159093B (en) | 2015-10-08 | 2015-10-08 | Microgrid energy Optimal Control System and its design method based on model adaptation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105159093A CN105159093A (en) | 2015-12-16 |
| CN105159093B true CN105159093B (en) | 2018-06-29 |
Family
ID=54799981
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510643423.XA Active CN105159093B (en) | 2015-10-08 | 2015-10-08 | Microgrid energy Optimal Control System and its design method based on model adaptation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105159093B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106097145A (en) * | 2016-06-15 | 2016-11-09 | 天津天成恒创能源科技有限公司 | Comprehensive energy network energy regulator control system |
| CN110401186A (en) * | 2019-07-13 | 2019-11-01 | 国网天津市电力公司 | Area power grid source net lotus stores up the ubiquitous Dispatching Control System of Multi-value coordination |
| CN111985781B (en) * | 2020-07-21 | 2023-12-19 | 浙江中新电力工程建设有限公司 | Multi-energy complementary micro-grid system based on reverse cooperative regulation and control and method thereof |
| CN112003330B (en) * | 2020-09-02 | 2022-05-17 | 浙江浙能技术研究院有限公司 | Adaptive control-based microgrid energy optimization scheduling method |
| CN113420910B (en) * | 2021-06-03 | 2024-02-02 | 南方电网数字电网科技(广东)有限公司 | Industrial and commercial intelligent power utilization control method, device, computer equipment and storage medium |
| CN113595238B (en) * | 2021-06-03 | 2022-05-31 | 长沙理工大学 | Intelligent power distribution cabinet and distribution box low-carbon polymerization regulation and control system, method and terminal device |
| CN113485126B (en) * | 2021-08-23 | 2023-05-12 | 安徽工业大学 | Improved dynamic matrix control three-time control method for direct-current micro-grid cluster |
| CN114217574A (en) * | 2021-10-12 | 2022-03-22 | 国网河北省电力有限公司正定县供电分公司 | Energy grid construction system and method based on energy and load matching |
| CN117273987B (en) * | 2023-11-21 | 2024-02-02 | 天津风霖物联网科技有限公司 | Data processing method and system for building automation system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103633739A (en) * | 2013-11-28 | 2014-03-12 | 中国科学院广州能源研究所 | Microgrid energy management system and method |
| CN104376389A (en) * | 2014-12-10 | 2015-02-25 | 国电南京自动化股份有限公司 | Master-slave type micro-grid power load prediction system and master-slave type micro-grid power load prediction method based on load balancing |
| CN104505864A (en) * | 2014-12-12 | 2015-04-08 | 国家电网公司 | Demand response control strategy simulation system and method for absorptive distributed type power generation |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3224641B2 (en) * | 1993-07-27 | 2001-11-05 | 中部電力株式会社 | Maximum power demand forecasting method |
| CN102751780B (en) * | 2012-07-03 | 2014-07-16 | 国电南瑞科技股份有限公司 | Wide area backup power auto-switch-on model self-adaptive generation method |
| CN103336876A (en) * | 2013-07-23 | 2013-10-02 | 国家电网公司 | Open loop distribution network power flow simulation method based on multi-agents |
-
2015
- 2015-10-08 CN CN201510643423.XA patent/CN105159093B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103633739A (en) * | 2013-11-28 | 2014-03-12 | 中国科学院广州能源研究所 | Microgrid energy management system and method |
| CN104376389A (en) * | 2014-12-10 | 2015-02-25 | 国电南京自动化股份有限公司 | Master-slave type micro-grid power load prediction system and master-slave type micro-grid power load prediction method based on load balancing |
| CN104505864A (en) * | 2014-12-12 | 2015-04-08 | 国家电网公司 | Demand response control strategy simulation system and method for absorptive distributed type power generation |
Non-Patent Citations (1)
| Title |
|---|
| "微电网监控与能量管理装置的设计与研发";查申森 等;《电力***自动化》;20140510;第38卷(第9期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105159093A (en) | 2015-12-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105159093B (en) | Microgrid energy Optimal Control System and its design method based on model adaptation | |
| CN105375479B (en) | A kind of distributed energy energy management method based on Model Predictive Control | |
| CN107742900A (en) | Wind-light storage intelligent micro-grid economic dispatch control method | |
| CN112072640B (en) | Capacity optimization method for virtual power plant polymerization resources | |
| Bansal et al. | Economic analysis and power management of a small autonomous hybrid power system (SAHPS) using biogeography based optimization (BBO) algorithm | |
| CN101769788B (en) | Method for forecasting optical output power and electric energy production of photovoltaic power station | |
| CN103631234B (en) | A kind of intelligent optimization dispatching method of cascade hydropower Masterstation Automation System | |
| CN107612017B (en) | Wind-electricity integration intelligent control system based on demand response and distributed energy storage | |
| CN102930351B (en) | A kind of synthesis energy saving Optimized Operation daily planning generation method | |
| CN109936170A (en) | Consider the honourable extreme misery complementation coordination optimization dispatching method of power supply flexibility nargin | |
| CN112651634B (en) | Active power distribution system source network load storage day-ahead active power scheduling method based on sequence operation | |
| CN107221965B (en) | Day-ahead plan calculation method based on distributed design | |
| Mousavi et al. | Modelling, design, and experimental validation of a grid-connected farmhouse comprising a photovoltaic and a pumped hydro storage system | |
| CN109740808A (en) | A kind of scene water complemental power-generation plan calculation method and system | |
| CN117239740B (en) | Optimal configuration and flexibility improvement method and system for virtual power plant system | |
| CN109474007B (en) | Energy internet scheduling method based on big data cloud technology | |
| CN103904664B (en) | A kind of AGC unit real-time scheduling method based on effective static security territory | |
| KR20130074045A (en) | A control method for controlling energy of building based ob microgrid and system for same | |
| CN107785929A (en) | A kind of solar energy thermal-power-generating station power prediction system and operation method | |
| CN104636824A (en) | Distributed photovoltaic siting and sizing method for determining minimum permeability | |
| Wang et al. | Compromise between cost and reliability in optimum design of an autonomous hybrid power system using mixed-integer PSO algorithm | |
| CN113869742B (en) | Comprehensive supply and demand side power dispatching system based on mobile home and commentator networks | |
| Mu et al. | A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model | |
| CN112350435A (en) | Virtual power plant management and control device based on micro-grid group and electric power controllable load | |
| Zhong et al. | A case study of operation optimization on a renewable energy building by E-CPS method: From both sides of supply and demand |
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 |