CN108667030A - A kind of polynary duty control method based on prediction model - Google Patents
A kind of polynary duty control method based on prediction model Download PDFInfo
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- CN108667030A CN108667030A CN201810371848.3A CN201810371848A CN108667030A CN 108667030 A CN108667030 A CN 108667030A CN 201810371848 A CN201810371848 A CN 201810371848A CN 108667030 A CN108667030 A CN 108667030A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of polynary duty control method based on prediction model, technical characterstic are：Include the following steps：Step 1 classifies to power load；Step 2 determines system control targe, decision variable and relevant constraints, forms the prediction model of polynary load optimal control；Step 3, the up-and-down boundary for determining each controlling cycle；Step 4, the subproblem for converting former optimization problem to each moment；Step 5, the distributed energy for obtaining the moment instantly are contributed, the related data of electricity price and controllable burden；Step 6, photovoltaic is contributed to be pre-allocated in each type load；Step 7 solves the subproblem at moment instantly by Liapunov optimization method, the decision variable at moment instantly is obtained, to form decision variable queue；Step 8, renewal time to subsequent time, terminate until entirely optimizing time interval.The economical operation of power distribution network of the present invention provides that a kind of cost is lower, the higher optimization method of feasibility.
Description
Technical field
The invention belongs to system for distribution network of power technical fields, are related to polynary duty control method, and especially one kind is based on
The polynary duty control method of prediction model.
Background technology
Power distribution network is renewable energy power generation and energy-storage system, is the network system that direct relation occurs with user demand,
It is also to coordinate consumption regenerative resource, the terminal power grid of balancing user workload demand.In the operational process of power distribution network, due to
The fluctuation of randomness and regenerative resource (photovoltaic/wind-powered electricity generation) output of family electricity consumption behavior, leads to following load data, new energy
Source power generation data, electricity price data are all difficult to accurately predict, the time variation of energy stream and information flow in power distribution network is made to increased dramatically,
The difficulty for ensureing the real-time power equilibrium of supply and demand is substantially increased, quickly and effectively energy hole is needed, makes user to Optimal Decision-making
Requirement of real-time it is more and more high.
Invention content
The purpose of the present invention is to provide a kind of reasonable design, cost is lower, feasibility is higher based on prediction model
Polynary duty control method.
The present invention solves its realistic problem and following technical scheme is taken to realize：
A kind of polynary duty control method based on prediction model, as shown in Figure 1, including the following steps：
Step 1 classifies to power load；
Step 2 determines system control targe, decision variable and relevant constraints, forms polynary load optimal control
Prediction model；
Step 3 is predicted for controllable burden, determines the up-and-down boundary of each controlling cycle；
Step 4, according to constraints, object function and controllable burden up-and-down boundary, convert former optimization problem to each
The subproblem at moment；
Step 5, the distributed energy for obtaining the moment instantly are contributed, the related data of electricity price and controllable burden；
Step 6 pre-allocates photovoltaic output according to certain principle in each type load；
Step 7 solves the subproblem at moment instantly by Liapunov optimization method, obtains moment instantly
Decision variable, to form decision variable queue；
Step 8, renewal time to subsequent time, return to step 6 terminate until entirely optimizing time interval.
Moreover, power load is divided into baseline load and two class of controllable burden by the step 1：
(1) baseline negative pocket includes the necessary load in business, production and life；
(2) controllable burden includes HVAC water heaters and electric vehicle.
Moreover, system Controlling object function is to minimize electric cost in the step 2, decision variable is that controllable burden is every
The power consumption at a moment, constraints include the power constraint of controllable burden.
Moreover, there are three variables for controllable burden in the step 3Respectively
HVAC, water heater and electric vehicle；Liapunov function in used Liapunov optimization method isRepresent the crowding of three load queues；Liapunov drift is defined asFor the phase of liapunov function value adjacent moment changing value
It hopes.
Moreover, the step 4 relieves coupling of the primal problem in time scale using Liapunov optimization method
Former problem is converted to the linear programming problem at each moment by relationship, according to future state predictive information and moment instantly
Correlated variables can be solved.
Moreover, the relevant parameter of controllable burden includes the rated power of controllable burden in the step 5, each moment with
Machine workload demand and user set the normal temperature of HVAC operations.
Moreover, the output power of photovoltaic meets baseline load first in the step 6, it, will be according to controllable if still there is residue
The priority of load is allocated successively with deferred constraint；The priority of controllable burden is water heater, HVAC and electric vehicle.
The advantages of the present invention：
1, it is based on the polynary spatial load forecasting side of prediction model the invention discloses a kind of in system for distribution network of power technical field
Method, by user power utilization cost minimization target as an optimization, by controllable burden HVAC, the power consumption of water heater and electric vehicle is made
For decision variable.The electric cost of user is made by adjusting the power consumption of controllable burden using Liapunov optimization method
It is minimum, and ensure the stability of system.Fully demonstrated in optimization process guiding function that electricity price is accustomed to user power utilization with
The Modulatory character of load, and consider the comfort level of user.Simulation result shows that the electric cost of user significantly reduces, controllably
The Modulatory character of load is fully excavated, and the efficiency and applicability of optimization method is demonstrated.It is proposed by the present invention polynary negative
Lotus control method, by the linear programming problem that complicated Global Optimal Problem decoupling is each moment, computation complexity is small；According to
Predictive information to the future state and correlated variables at moment can solve instantly, calculates at low cost, and feasibility is high；It will use
Electric load is divided into baseline load and controllable burden two parts, embodies the Modulatory character of load；The target temperature of HVAC is set,
Users'comfort is considered, provides that a kind of cost is lower, the higher optimization method of feasibility for the economical operation of power distribution network.
Complicated Global Optimal Problem decoupling is each moment by 2, power distribution network operational control method proposed by the present invention
Linear programming problem, computation complexity are small；According to system to the predictive information of future state, in conjunction with the correlated variables at moment instantly
It can be solved, be calculated at low cost；Power load is divided into baseline load and controllable burden two parts, embody load can
Control；Set the target temperature of HVAC, it is contemplated that users'comfort provides a kind of cost for the economical operation of power distribution network
Lower, the higher optimization method of feasibility.
3, the present invention passes through the new energy output to future time instance, the prediction of load and electricity price, in conjunction with moment distribution instantly
Various states in net system can show that the moment to the regulation and control decision of power distribution network, provides for the economical operation of power distribution network instantly
A kind of scheme at low cost, feasibility is high.
Description of the drawings
Fig. 1 is the control method building-block of logic of the present invention；
Fig. 2 is the flow diagram based on the polynary duty control method of prediction model of the present invention；
Fig. 3 is the front and back run time comparison diagram of the water heater optimization of the present invention；
Fig. 4 is the front and back run time comparison diagram of the electric vehicle optimization of the present invention；
Fig. 5 is the front and back run time comparison diagram of the HVAC optimizations of the present invention；
Fig. 6 is the comparison diagram of indoor temperature and outdoor temperature after the HVAC of the present invention optimizes.
Specific implementation mode
The embodiment of the present invention is described in further detail below in conjunction with attached drawing：
A kind of polynary duty control method based on prediction model, as shown in Fig. 2, including the following steps：
Step 1 classifies to power load；
Power load is divided into baseline load and two class of controllable burden by the step 1：
(1) baseline load is needed to start immediately and cannot be interrupted, and can not be regulated and controled, including business, production and life in must
It must load；
(2) controllable burden need not start immediately, can also arbitrarily be interrupted after startup, including HVAC (Heating,
Ventilation and Air Conditioning, heat supply, ventilation and air-conditioning), water heater and electric vehicle etc..
Step 2 determines system control targe, decision variable and relevant constraints, forms polynary load optimal control
Prediction model；
In the step 2 system Controlling object function be minimize electric cost, decision variable be controllable burden (HVAC,
Water heater, electric vehicle) each moment power consumption, constraints includes the power constraint of controllable burden.
Step 3 is predicted for controllable burden, determines the up-and-down boundary of each controlling cycle；
There are three variables for controllable burden in the step 3Respectively HVAC, heat
Hydrophone and electric vehicle；
Liapunov function in used Liapunov optimization method isIt represents
The crowding of three load queues；
Liapunov drift is defined asFor Li Yapunuo
The expectation of husband's functional value adjacent moment changing value；
Step 4, according to constraints, object function and controllable burden up-and-down boundary, convert former optimization problem to each
The subproblem at moment；
The step 4 relieves coupled relation of the primal problem in time scale using Liapunov optimization method,
The linear programming problem that former problem is converted to each moment is related to the moment instantly according to the predictive information to future state
Variable can be solved.
Step 5, the distributed energy for obtaining the moment instantly are contributed, the related data of electricity price and controllable burden；
The relevant parameter of controllable burden includes the rated power of controllable burden in the step 5, and the random of each moment is born
Lotus demand and user set the normal temperature of HVAC operations.
Step 6 pre-allocates photovoltaic output according to certain principle in each type load；
The output power of photovoltaic meets baseline load first in the step 6, will be according to controllable burden if still there is residue
Priority is allocated successively with deferred constraint；The priority of controllable burden is water heater, HVAC and electric vehicle.
Step 7 solves the subproblem at moment instantly by Liapunov optimization method, obtains moment instantly
Decision variable, to form decision variable queue；
Step 8, renewal time to subsequent time, return to step 6 terminate until entirely optimizing time interval.
Fig. 1 is control method building-block of logic, and figure includes photovoltaic generation unit and power load etc..In the present embodiment
It is used in computer sim- ulation based on renewable energy power generation and power grid power supply.Photovoltaic generation unit is by photovoltaic array and photovoltaic DC-to-AC converter
Composition.The function of wherein photovoltaic cell is to convert the solar into electric energy, and inverter mainly becomes the sent out direct current of photovoltaic
Alternating current is used for load.Power load includes baseline load and controllable burden, and it is controllable to illustrate regulation and control object-for emphasis in figure
Load HVAC, water heater and electric vehicle.Using power controller as core in figure.Measurement is collected from the user with modeling module
Workload demand, photovoltaic generation go out force data, and the relevant parameter of power grid electricity price data and controllable burden is modeled.Power control
Device is optimized after obtaining these information using Liapunov optimization method, is regulated and controled to controllable burden.
Fig. 2 is the flow diagram based on the polynary duty control method of prediction model.Operation is optimized using the power distribution network in Fig. 2
Method, the effect that can reach are, by adjusting the run time of controllable burden, to keep the electric cost of user minimum, and ensure
The stability of system.The controllable of guiding function that electricity price is accustomed to user power utilization and load has been fully demonstrated in optimization process
Property, and consider the comfort level of user.Complicated Global Optimal Problem decoupling is every by the Liapunov optimization method used
The linear programming problem at a moment, computation complexity are small；Become according to the predictive information to future state is related to the moment instantly
Amount can be solved, and at low cost, feasibility height is calculated.
The electric cost comparison such as table 1 of distribution network users：
Table 1：The front and back electric cost comparison of optimization
Fig. 3 is the front and back run time comparison diagram of water heater optimization；From simulation result it can be seen that：Fig. 3 is that water heater is excellent
Change front and back run time comparison diagram.Maximum delay time is 10 minutes, can't influence the comfort level of user.
Fig. 4 is the front and back run time comparison diagram of electric vehicle optimization.Sub-load is shifted after optimization, avoids high electricity
The valence period.Maximum delay time is 50 minutes, has no effect on the normal use of user.
Fig. 5 is the front and back run time comparison diagram of HVAC optimizations.Maximum delay time is 30 minutes, and sub-load is turned
It moves, avoids high rate period.
Fig. 6 is the comparison diagram of indoor temperature and outdoor temperature after HVAC optimizations.As can be seen that indoor temperature change generated in case is 19 DEG C
To 22 DEG C, the comfort level of user ensure that.Understand that the final optimization pass result of HVAC remains to base on the basis of reducing electric cost
This meets the temperature requirements of user.
Therefore, of the invention based on the polynary duty control method of prediction model, when can be by adjusting the operation of controllable burden
Between, keep the electric cost of user minimum, and ensure the stability of system.Electricity price has been fully demonstrated in optimization process to use user
The Modulatory character of the guiding function and load of electricity custom, and consider the comfort level of user.The Liapunov optimization side of use
For method by the linear programming problem that complicated Global Optimal Problem decoupling is each moment, computation complexity is small；According to the following shape
The correlated variables at the predictive information of state and instantly moment can be solved, and at low cost, feasibility height is calculated.
The present invention is a kind of in renewable energy power generation, the lower quickly regulation and control energy of arbitrary fluctuation of customer charge and power grid electricity price
Amount is to improve the on-line Algorithm of user utility function, in the on-line optimization algorithm, does not depend on any following generated energy, load, electricity
The data of valence, it is only necessary to which the various states obtained in moment distribution network system instantly can show that the moment is to the tune of power distribution network instantly
Control decision.As the optimal controller of intelligent grid, the development of the communication technology, power distribution network can obtain the interaction letter of various aspects online
Breath, that has established on-line Algorithm executes basis.On-line optimization will be as one of the development trend of the following power distribution network energy management.
It is emphasized that embodiment of the present invention is illustrative, without being restrictive, therefore the present invention includes
It is not limited to embodiment described in specific implementation mode, it is every to be obtained according to the technique and scheme of the present invention by those skilled in the art
Other embodiment, also belong to the scope of protection of the invention.
Claims (7)
1. a kind of polynary duty control method based on prediction model, it is characterised in that：Include the following steps：
Step 1 classifies to power load；
Step 2 determines system control targe, decision variable and relevant constraints, forms the pre- of polynary load optimal control
Survey model；
Step 3 is predicted for controllable burden, determines the up-and-down boundary of each controlling cycle；
Step 4, according to constraints, object function and controllable burden up-and-down boundary, convert former optimization problem to each moment
Subproblem；
Step 5, the distributed energy for obtaining the moment instantly are contributed, the related data of electricity price and controllable burden；
Step 6 pre-allocates photovoltaic output according to certain principle in each type load；
Step 7 solves the subproblem at moment instantly by Liapunov optimization method, obtains the decision at moment instantly
Variable, to form decision variable queue；
Step 8, renewal time to subsequent time, return to step 6 terminate until entirely optimizing time interval.
2. a kind of polynary duty control method based on prediction model according to claim 1, it is characterised in that：The step
Power load is divided into baseline load and two class of controllable burden by rapid 1：
(1) baseline negative pocket includes the necessary load in business, production and life；
(2) controllable burden includes HVAC water heaters and electric vehicle.
3. a kind of polynary duty control method based on prediction model according to claim 1 or 2, it is characterised in that：Institute
It is to minimize electric cost to state system Controlling object function in step 2, and decision variable is that the power at controllable burden each moment disappears
Consumption, constraints includes the power constraint of controllable burden.
4. a kind of polynary duty control method based on prediction model according to claim 1 or 2, it is characterised in that：Institute
Stating controllable burden in step 3, there are three variablesRespectively HVAC, water heater and electronic vapour
Vehicle；Liapunov function in used Liapunov optimization method isThree are represented to bear
The crowding of lotus queue；Liapunov drift is defined asFor
The expectation of liapunov function value adjacent moment changing value.
5. a kind of polynary duty control method based on prediction model according to claim 1 or 2, it is characterised in that：Institute
It states step 4 and coupled relation of the primal problem in time scale is relieved using Liapunov optimization method, former problem is turned
Be changed to the linear programming problem at each moment, according to future state predictive information and the correlated variables at moment can be into instantly
Row solves.
6. a kind of polynary duty control method based on prediction model according to claim 1 or 2, it is characterised in that：Institute
The relevant parameter for stating controllable burden in step 5 includes the rated power of controllable burden, the random load demand and use at each moment
Family sets the normal temperature of HVAC operations.
7. a kind of polynary duty control method based on prediction model according to claim 1 or 2, it is characterised in that：Institute
The output power for stating photovoltaic in step 6 meets baseline load first, if still there is residue, by according to the priority of controllable burden with prolong
Late binding is allocated successively；The priority of controllable burden is water heater, HVAC and electric vehicle.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112613656A (en) * | 2020-12-18 | 2021-04-06 | 国网山东省电力公司青岛市即墨区供电公司 | Household power demand response optimization system based on fish swarm algorithm |
| CN114228740A (en) * | 2021-10-26 | 2022-03-25 | 北京触达无界科技有限公司 | Vehicle control method, vehicle control device, vehicle and storage medium |
| CN116316647A (en) * | 2022-09-08 | 2023-06-23 | 东南大学溧阳研究院 | Model predictive control-based real-time carbon emission optimization control method for power distribution network |
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