CN105322550A - Optimization method for household micro-grid operation - Google Patents
Optimization method for household micro-grid operation Download PDFInfo
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- CN105322550A CN105322550A CN201510540027.4A CN201510540027A CN105322550A CN 105322550 A CN105322550 A CN 105322550A CN 201510540027 A CN201510540027 A CN 201510540027A CN 105322550 A CN105322550 A CN 105322550A
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- 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
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- 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
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- 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
-
- 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
- Y04S20/242—Home appliances
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- Air Conditioning Control Device (AREA)
- Control Of Electrical Variables (AREA)
Abstract
The invention discloses an optimization method for a household micro-grid operation. The method comprises the following steps: (1) classifying household electrical appliances; (2) determining a system target function, a decision variable and relevant constraint conditions to form an original global optimization problem; (3) determining a lyapunov function and a lyapunov drift aiming at a controllable load queue; (4) converting the original global optimization problem into a sub-problem at each moment according to a lyapunov optimization method; (5) obtaining related data of photovoltaic output, electricity price and controllable loads at the current moment; (6) carrying out a pre-distribution on the photovoltaic output in various loads according to a certain principle; (7) solving the sub-problem at the current moment to obtain the decision variable at the current moment; and (8) updating the time into the next moment, and returning to the step (6) until the whole optimization time interval is ended. According to the optimization method for the household micro-grid operation disclosed by the invention, a regulation and control decision on the micro-grid at the current moment can be obtained only by obtaining various states in a micro-grid system at the current moment.
Description
Technical field
The present invention relates to electric power system micro-capacitance sensor, more particularly, relate to the optimization method that a kind of household micro-capacitance sensor runs.
Background technology
Micro-capacitance sensor is that electric power system provides a kind of renewable energy power generation and energy-storage system, and being a kind of distributed collection form of user's request, is also the ideal platform of distributed coordination regenerative resource and customer charge.In the running of micro-capacitance sensor, due to the fluctuation that randomness and the regenerative resource (photovoltaic/wind-powered electricity generation) of user power utilization behavior export, following load data, generation of electricity by new energy data, electricity price data are all difficult to predict exactly, the time variation of energy flow and information flow in the micro-capacitance sensor of user side is sharply increased, substantially increase the difficulty ensureing the real-time power equilibrium of supply and demand, need energy hole fast and effectively, make user more and more high to the requirement of real-time of Optimal Decision-making.Therefore, need a kind ofly under any fluctuation of renewable energy power generation, customer charge and electrical network electricity price, to regulate and control fast energy to improve the method for on-line optimization of user utility function.
Summary of the invention
The object of the invention is to: provide the optimization method that household micro-capacitance sensor runs, the various states only needing to obtain in moment micro-grid system instantly can show that the moment is to the regulation and control decision-making of micro-capacitance sensor instantly.
To achieve these goals, the invention provides the optimization method that a kind of household micro-capacitance sensor runs, it comprises the steps:, and (1) classifies to household electric appliances; (2) certainty annuity target function, decision variable and relevant constraints, form original Global Optimal Problem; (3) for controllable burden queue, determine that liapunov function and Liapunov drift about; (4) according to Liapunov optimization method, original Global Optimal Problem is converted into the subproblem in each moment; (5) photovoltaic obtaining the moment is instantly exerted oneself, electricity price, the related data of controllable burden; (6) photovoltaic is exerted oneself and in each type load, carry out preassignment according to certain principle; (7) subproblem in moment is instantly solved, obtain the decision variable in moment instantly; (8) update time is to subsequent time, returns step 6, until whole optimization time interval terminates.
As a modification of the present invention, in step (1), described household electric appliances is divided into baseline load and controllable burden, described baseline load is need to start immediately and the equipment that can not interrupt, and described controllable burden is do not need to start immediately and also can by the equipment arbitrarily interrupted after starting.
As a modification of the present invention, described baseline negative pocket draws together illumination, computer, TV, recreational facilities and refrigerator, described controllable burden comprises HVAC (Heating, VentilationandAirConditioning, heat supply, ventilation and air-conditioning), water heater and electric automobile.
As a modification of the present invention, in step (2), described system goal function is for minimizing electric cost, and described decision variable is the power consumption in controllable burden each moment, and described constraints comprises the power constraint of controllable burden.
As a modification of the present invention, in step (3), described controllable burden queue has three queues
be respectively HVAC, water heater and electric automobile, liapunov function is
represent the crowding of three load queues, Liapunov drift is defined as
for the expectation of liapunov function value adjacent moment changing value.
As a modification of the present invention, in step (5), the relevant parameter of described controllable burden comprises the normal temperature that the rated power of controllable burden, the random load demand in each moment and user set HVAC operation.
As a modification of the present invention, in step (6), first the power output of described photovoltaic meets baseline load, if the residue of still having, distribute according to the priority of controllable burden and deferred constraint successively, the priority of controllable burden is followed successively by water heater, HVAC, electric automobile.
Compared with prior art, the optimization method that household micro-capacitance sensor of the present invention runs is at renewable energy power generation, energy is regulated and controled fast to improve user utility under any fluctuation of customer charge and electrical network electricity price, do not rely on any following energy output, load, the data of electricity price, the various states only needing to obtain in moment micro-grid system instantly can show that the moment is to the regulation and control decision-making of micro-capacitance sensor instantly.Along with intelligent grid, the development of the communication technology, the present invention is the scheme that the economical operation of micro-capacitance sensor provides that a kind of cost is low, feasibility is high.
Accompanying drawing explanation
Below in conjunction with the drawings and specific embodiments, the solution of the present invention and Advantageous Effects thereof are described in detail.
Fig. 1 is the optimization method flow chart that household micro-capacitance sensor of the present invention runs.
Fig. 2 is micro-grid system composition diagram of the present invention.
Fig. 3 is comparison diagram running time before and after water heater is optimized.
Fig. 4 is comparison diagram running time before and after electric automobile is optimized.
Fig. 5 is comparison diagram running time before and after HVAC optimizes.
Fig. 6 is the comparison diagram of indoor temperature and outdoor temperature after HVAC optimizes.
Embodiment
In order to make goal of the invention of the present invention, technical scheme and Advantageous Effects thereof more clear, below in conjunction with the drawings and specific embodiments, the present invention is further elaborated.Should be understood that, the embodiment described in this specification is only used to explain the present invention, is not intended to limit the present invention.
Refer to Fig. 1, the optimization method that household micro-capacitance sensor of the present invention runs comprises the following steps:
Step 1: household electric appliances is classified;
Step 2: certainty annuity target function, decision variable and relevant constraints, form original Global Optimal Problem;
Step 3: for controllable burden queue, determines that liapunov function and Liapunov drift about;
Step 4: the subproblem according to Liapunov optimization method, original Global Optimal Problem being converted into each moment;
Step 5: the photovoltaic obtaining the moment is instantly exerted oneself, electricity price, the related data of controllable burden;
Step 6: photovoltaic is exerted oneself and carries out preassignment according to certain principle in each type load;
Step 7: the subproblem in moment is instantly solved, obtains the decision variable in moment instantly.
Step 8: update time, to subsequent time, returns step 6, until whole optimization time interval terminates.
In step 1, be that household electric appliances is divided into baseline load and controllable burden to the classification of household electric appliances, wherein baseline load needs to start immediately and can not interrupt, and can not regulate and control, comprise illumination, computer, TV, recreational facilities, refrigerator etc.Controllable burden does not need to start immediately, can arbitrarily be interrupted after startup yet, comprises HVAC (Heating, VentilationandAirConditioning, heat supply, ventilation and air-conditioning), water heater and electric automobile.
In step 2, system goal function is for minimizing electric cost, and decision variable is the power consumption in controllable burden (HVAC, water heater, electric automobile) each moment, and constraints comprises the power constraint etc. of controllable burden.
In step 3, controllable burden queue has three queues
be respectively HVAC, water heater and electric automobile.Liapunov function is
represent the crowding of three load queues.Liapunov drift is defined as
for the expectation of liapunov function value adjacent moment changing value.
In step 4, Liapunov optimization method relieves the coupled relation of primal problem in time scale, by the linear programming problem that former question variation is each moment, the information in any future in not dependence system, only needs the correlated variables in moment instantly to solve.
In steps of 5, the relevant parameter of controllable burden comprises the rated power of controllable burden, the random load demand in each moment, and user sets the normal temperature of HVAC operation.
In step 6, first the power output of photovoltaic meets baseline load.If the residue of still having, distribute according to the priority of controllable burden and deferred constraint successively.The priority of controllable burden is water heater, HVAC, electric automobile.
Refer to Fig. 2, micro-grid system composition of the present invention comprises photovoltaic generation unit and power load etc.In the computer sim-ulation of the present embodiment, adopt renewable energy power generation and mains supply to be main.Photovoltaic generation unit is made up of photovoltaic array and photovoltaic DC-to-AC converter.Wherein the function of photovoltaic cell is that solar energy is converted to electric energy, inverter mainly photovoltaic send out direct current and become alternating current, for load.Power load comprises baseline load and controllable burden, and in figure, emphasis illustrates regulation and control object-controllable burden HVAC, water heater and electric automobile.Be core with power controller in figure.Measure and collect workload demand from user with MBM, photovoltaic generation go out force data, the relevant parameter of electrical network electricity price data and controllable burden carries out modeling.Power controller adopts Liapunov optimization method to be optimized after these information of acquisition, regulates and controls controllable burden.
The optimization method that household micro-capacitance sensor of the present invention runs, by the running time of adjustment controllable burden, makes the electric cost of user minimum, and ensures the stability of system.In optimizing process, fully demonstrate the Modulatory character of guiding function that electricity price is accustomed to user power utilization and load, and consider the comfort level of user.The Global Optimal Problem decoupling zero of complexity is the linear programming problem in each moment by the Liapunov optimization method adopted, and computation complexity is little; Do not need the information in any future in system, only need the correlated variables in moment instantly to solve, assess the cost low, the economical operation for household micro-capacitance sensor provides the optimization method that a kind of cost is lower, feasibility is higher.
The electric cost of micro-capacitance sensor user is to such as table 1:
| Electric cost before optimizing, RMB | Electric cost after optimizing, RMB | Optimization rate, % |
| 43.8618 | 36.9227 | 15.82 |
Table 1: electric cost contrast before and after optimizing
Simulation calculation adopts the micro-grid system shown in Fig. 1.Can see from simulation result: (1) refers to Fig. 3, for comparison diagram running time of front and back optimized by water heater, maximum time of delay is 10 minutes, can't affect the comfort level of user.(2) Fig. 4 is referred to, for comparison diagram running time of front and back optimized by electric automobile.After optimization, sub-load is shifted, avoid high rate period.Maximum time of delay is 50 minutes, does not affect the normal use of user.(4) Fig. 5 is referred to, for HVAC optimizes comparison diagram running time of front and back.Maximum time of delay is 30 minutes, sub-load is shifted, avoids high rate period.(5) referring to Fig. 6, is the comparison diagram of indoor temperature and outdoor temperature after HVAC optimizes.Can find out, indoor temperature change generated in case is 19 DEG C to 22 DEG C, ensure that the comfort level of user.The final optimization pass result of known HVAC still substantially can meet the temperature requirements of user on the basis reducing electric cost.
Therefore, adopt the household micro-capacitance sensor running optimizatin method optimized based on Liapunov, by adjusting the running time of controllable burden, making the electric cost of user minimum, and ensureing the stability of system.In optimizing process, fully demonstrate the Modulatory character of guiding function that electricity price is accustomed to user power utilization and load, and consider the comfort level of user.The Global Optimal Problem decoupling zero of complexity is the linear programming problem in each moment by the Liapunov optimization method adopted, and computation complexity is little; Do not need the information in any future in system, only need the correlated variables in moment instantly to solve, assess the cost low, the economical operation for household micro-capacitance sensor provides the optimization method that a kind of cost is lower, feasibility is higher.
The announcement of book and instruction according to the above description, those skilled in the art in the invention can also carry out suitable change and amendment to above-mentioned execution mode.Therefore, the present invention is not limited to embodiment disclosed and described above, also should fall in the protection range of claim of the present invention modifications and changes more of the present invention.In addition, although employ some specific terms in this specification, these terms just for convenience of description, do not form any restriction to the present invention.
Claims (7)
1. an optimization method for household micro-capacitance sensor operation, it is characterized in that, it comprises the steps:
(1) household electric appliances is classified;
(2) certainty annuity target function, decision variable and relevant constraints, form original Global Optimal Problem;
(3) for controllable burden queue, determine that liapunov function and Liapunov drift about;
(4) according to Liapunov optimization method, original Global Optimal Problem is converted into the subproblem in each moment;
(5) photovoltaic obtaining the moment is instantly exerted oneself, electricity price, the related data of controllable burden;
(6) photovoltaic is exerted oneself and in each type load, carry out preassignment according to certain principle;
(7) subproblem in moment is instantly solved, obtain the decision variable in moment instantly;
(8) update time is to subsequent time, returns step 6, until whole optimization time interval terminates.
2. the optimization method of household micro-capacitance sensor operation according to claim 1, it is characterized in that, in step (1), described household electric appliances is divided into baseline load and controllable burden, described baseline load is need to start immediately and the equipment that can not interrupt, and described controllable burden is do not need to start immediately and also can by the equipment arbitrarily interrupted after starting.
3. the optimization method of household micro-capacitance sensor operation according to claim 2, it is characterized in that, described baseline negative pocket draws together illumination, computer, TV, recreational facilities and refrigerator, and described controllable burden comprises HVAC, water heater and electric automobile.
4. the optimization method of household micro-capacitance sensor operation according to claim 2, it is characterized in that, in step (2), described system goal function is for minimizing electric cost, described decision variable is the power consumption in controllable burden each moment, and described constraints comprises the power constraint of controllable burden.
5. the optimization method of household micro-capacitance sensor operation according to claim 3, it is characterized in that, in step (3), described controllable burden queue has three queues
be respectively HVAC, water heater and electric automobile, liapunov function is
represent the crowding of three load queues, Liapunov drift is defined as
for the expectation of liapunov function value adjacent moment changing value.
6. the optimization method of household micro-capacitance sensor operation according to claim 3, it is characterized in that, in step (5), the relevant parameter of described controllable burden comprises the normal temperature that the rated power of controllable burden, the random load demand in each moment and user set HVAC operation.
7. the optimization method of household micro-capacitance sensor operation according to claim 3, it is characterized in that, in step (6), first the power output of described photovoltaic meets baseline load, if the residue of still having, distribute according to the priority of controllable burden and deferred constraint successively, the priority of controllable burden is followed successively by water heater, HVAC, electric automobile.
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| CN107218701A (en) * | 2017-06-09 | 2017-09-29 | 河海大学 | A kind of air conditioner load group's distributed control method optimized based on Liapunov |
| CN107248755A (en) * | 2017-07-25 | 2017-10-13 | 华中科技大学 | A kind of data center's regenerative resource smooths method of supplying power to |
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| CN108021029A (en) * | 2017-11-17 | 2018-05-11 | 北京航空航天大学 | A kind of intelligent domestic electricity demanding response platform |
| CN108494012A (en) * | 2018-01-31 | 2018-09-04 | 浙江工业大学 | A kind of meter and the electric regional complex energy resource system method for on-line optimization for turning gas technology |
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| CN108667030A (en) * | 2018-04-24 | 2018-10-16 | 国网天津市电力公司电力科学研究院 | A kind of polynary duty control method based on prediction model |
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| CN110752629B (en) * | 2019-10-25 | 2021-07-27 | 中民新能投资集团有限公司 | Energy optimization management method for AC/DC hybrid household micro-grid |
| CN113937802A (en) * | 2021-09-10 | 2022-01-14 | 南京南瑞继保电气有限公司 | Micro-grid real-time scheduling method and device based on Lyapunov optimization |
| CN113988402A (en) * | 2021-10-25 | 2022-01-28 | 国网山东省电力公司青岛供电公司 | Optimal scheduling method and system for smart home of micro-grid group |
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