CN105811457A - Method for smoothing power of grid-connected microgrid tie line - Google Patents
Method for smoothing power of grid-connected microgrid tie line Download PDFInfo
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- 238000009499 grossing Methods 0.000 title abstract 2
- 238000004146 energy storage Methods 0.000 claims abstract description 92
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 230000005611 electricity Effects 0.000 claims description 64
- 239000002355 dual-layer Substances 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
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Classifications
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- H02J3/383—
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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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
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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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
- Y02P80/14—District level solutions, i.e. local energy networks
Abstract
The invention provides a method for smoothing a power of a grid-connected microgrid tie line. Double-layer optimal scheduling is adopted; the two-layer optimal scheduling comprises upper layer optimization for carrying out optimal scheduling on the basis of predicted data and lower layer optimization for carrying out real-time control according to collected real-time data. In a double-layer control optimal scheduling way, control on the power of the grid-connected microgrid tie line can be achieved; power fluctuation of the tie line is controlled to be within the appointed range; relatively large fluctuation of the power of the tie line caused by photovoltaic power generation instability is avoided; and the influence on the stability of a large grid after the microgrid is connected to the large grid is reduced. The capacity for connecting photovoltaics of the large power grid can be improved by reducing the fluctuation of the tie line; and the utilization rate of clean energy is improved. Through control on the electric quantity of an energy storage battery, over-charge and over-discharge of the battery can be prevented; and the service lifetime of the energy storage battery can be prolonged.
Description
Technical field
The invention belongs to light storage micro-grid system and run control technical field, relate to the adjustment of the adjustment of micro-capacitance sensor dominant eigenvalues, energy storage discharge and recharge, it is achieved the smooth control of grid type light storage micro-capacitance sensor dominant eigenvalues.
Background technology
Numerous research has been had to by photovoltaic, energy storage and traffic control method that power load is the intelligent micro-grid mainly comprised, there is the research based on economy scheduling, having the research controlled based on stability, these researchs mainly consider from micro-capacitance sensor self.But due to the uncertainty of photovoltaic generation and load electricity consumption, cause micro-capacitance sensor interconnection tie power fluctuation relatively big, lack a kind of effective method always and solve the problem that bulk power grid stability is impacted by micro-capacitance sensor.
Summary of the invention
In view of this, the present invention proposes a kind of method that grid type micro-capacitance sensor dominant eigenvalues is smooth, predict based on photovoltaic generation, load power consumption prediction, the electricity of energy-storage battery, the data such as current dominant eigenvalues, calculate the dispatch command of future scheduling cycle dominant eigenvalues, this dispatch command both can guarantee that interconnection tie power fluctuation was less than appointment scope, can effectively safeguard again the electricity of energy-storage battery, the reliability that micro-capacitance sensor is powered is ensured when bulk power grid has a power failure, the utilization rate of photovoltaic generation can also be improved, prevent and abandon optical issue, taken into account the stability of micro-capacitance sensor simultaneously, the economy of micro-capacitance sensor and the stability of bulk power grid.
For reaching above-mentioned purpose, the technical scheme is that and be achieved in that: a kind of method that grid type micro-capacitance sensor dominant eigenvalues is smooth, adopt dual-layer optimization scheduling；The scheduling of described dual-layer optimization includes being optimized based on prediction data the upper strata of scheduling and optimizes, and carries out lower floor's optimization of control in real time according to the real time data collected；
Described prediction data includes prediction photovoltaic generation power and prediction load generated output；Prediction data is accurate, then the result that dominant eigenvalues optimizes according to upper strata is run；Prediction data and actual data deviation are relatively big, then control optimization in real time by lower floor and carry out second-order correction.
Further, described upper strata optimizes, and defines following variable,
PpccNow: current dominant eigenvalues
Ppv: photovoltaic prediction generated output
Pload: load prediction power
SOCnow: current energy storage electricity
SOCup: the energy storage electricity upper limit
SOCdown: energy storage electricity lower limit
PesLimit: energy storage charge-discharge electric power limit value
Prange: interconnection tie power fluctuation value range
Pes: energy storage charge-discharge electric power
Ppcc: next dispatching cycle dominant eigenvalues
Its method particularly includes:
(1) electricity of current energy-storage battery is judged；
If current energy-storage battery electricity is higher than energy storage electricity upper limit SOCup, overcharge to ensure that battery occurs without, need next dispatching cycle to consider battery is discharged, and calculate the energy storage charge-discharge electric power Pes of battery；
If current energy-storage battery electricity is lower than energy storage electricity lower limit SOCdown, put to ensure that battery occurred without, need next dispatching cycle to consider battery is charged, and calculate the energy storage charge-discharge electric power Pes of battery；
If energy-storage battery electricity is between energy storage electricity upper limit SOCup and energy storage electricity lower limit SOCdown, it is not necessary to the electricity of battery is carried out special maintenance, calculates the energy storage charge-discharge electric power Pes of battery；
(2) battery charging and discharging limit value correction, for ensure energy-storage battery charge-discharge electric power less than energy storage discharge and recharge limit value PesLimit, when Pes is more than PesLimit, Pes is set to PesLimit, when Pes is less than-PesLimit, Pes is set to-PesLimit；
(3) the dominant eigenvalues instruction of optimization next dispatching cycle, computing formula is:
Ppcc=Pload-Ppv-Pes.
Further, in step (1), the computational methods of Pes are as follows:
(101) when energy-storage battery electricity is higher than energy storage electricity upper limit SOCup, in order to avoid energy-storage battery overcharges control, the charge-discharge electric power Pes arranging next of battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid dispatching cycle, and the maximum fluctuation amount allowed according to interconnection reduces and buys electrical power from bulk power grid, namely the charge-discharge electric power formula of battery is:
Pes=Pload-Ppv-PpccNow+Prange；
(102) when energy-storage battery electricity is lower than energy storage electricity lower limit SOCdown, control is put for avoiding energy-storage battery to cross, the charge-discharge electric power Pes arranging next of battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid dispatching cycle, and the maximum fluctuation amount allowed according to interconnection increases and buys electrical power from bulk power grid, namely the charge-discharge electric power formula of battery is:
Pes=Pload-Ppv-PpccNow-Prange；
(103) when energy-storage battery electricity is between energy storage electricity upper limit SOCup and energy storage electricity lower limit SOCdown, the charge-discharge electric power Pes arranging next of battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid dispatching cycle, and namely the charge-discharge electric power formula of battery is:
Pes=Pload-Ppv-PpccNow.
Further, described lower floor optimizes, method particularly includes: optimize the dominant eigenvalues instruction obtained according to upper strata, lower floor controls in real time；The power that target is interconnection controlled in real time fluctuates centered by Ppcc, and fluctuation range is less than interconnection fluctuation range value Prange.
Further, when photovoltaic generation prediction and load prediction deviation are less, real-time control does not export control instruction.
Further, when fluctuation range exceedes interconnection fluctuation range value Prange, control in real time to ensure that interconnection tie power fluctuation is not out-of-limit by adjustment energy storage charge-discharge electric power, switching photovoltaic.
Relative to prior art, the method that a kind of grid type micro-capacitance sensor dominant eigenvalues of the present invention smooths has the advantage that
The method that grid type micro-capacitance sensor dominant eigenvalues of the present invention smooths control, the Optimized Operation mode that application bilayer controls, it is capable of the control of grid type micro-capacitance sensor dominant eigenvalues, control the fluctuation of dominant eigenvalues less than the scope specified, prevent dominant eigenvalues from because of the unstability of photovoltaic generation, bigger fluctuation occurring, reduce micro-capacitance sensor and access after bulk power grid the impact on bulk power grid stability.By reducing the fluctuation of interconnection, it is possible to improve bulk power grid and access the capacity of photovoltaic, improve the utilization rate of clean energy resource.By the control to energy-storage battery electricity, it is possible to prevent over-charging of battery or excessively put, it is possible to extending the service life of energy-storage battery.
Accompanying drawing explanation
The accompanying drawing constituting the part of the present invention is used for providing a further understanding of the present invention, and the schematic description and description of the present invention is used for explaining the present invention, is not intended that inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the overview flow chart of the present invention.
Fig. 2 is the upper strata Optimized Operation flow chart of the present invention.
Detailed description of the invention
It should be noted that when not conflicting, embodiments of the invention and the feature in embodiment can be mutually combined.
Describe the present invention below with reference to the accompanying drawings and in conjunction with the embodiments in detail.
As shown in Figure 1, the present invention is in specific embodiment is applied, adopt 15 minutes as a dispatching cycle, then overall procedure is such that it is first determined whether arrive whole 15 minutes, if it is, photovoltaic generation power and load electric power to next 15 minutes are made prediction, then the method according to the invention, generate the dominant eigenvalues instruction of next 15 minutes, in next 15 minutes, control micro-capacitance sensor in real time according to dominant eigenvalues instruction；While continuing to judge whether by whole 15 minutes, if it is not, continue to control micro-capacitance sensor in real time according to dominant eigenvalues instruction, if it is, re-execute this flow process.
In above-mentioned flow process, generate the dominant eigenvalues instruction of next 15 minutes, the method for employing, illustrate as follows:
The present invention adopts dual-layer optimization dispatching method, and upper strata is optimized scheduling based on prediction data, and lower floor controls in real time according to the real time data collected.Generate electricity more accurately in situation at photovoltaic generation and load, the result that dominant eigenvalues optimizes according to upper strata is run, only when prediction data is bigger with actual data deviation, controlled in real time by lower floor to carry out second-order correction, pass through dual-layer optimization, can effectively control the smoothness of dominant eigenvalues, reduce interconnection fluctuation.
For ease of describing, following variable is defined
PpccNow: current dominant eigenvalues
Ppv: photovoltaic prediction generated output
Pload: load prediction power
SOCnow: current energy storage electricity
SOCup: the energy storage electricity upper limit
SOCdown: energy storage electricity lower limit
PesLimit: energy storage charge-discharge electric power limit value
Prange: interconnection tie power fluctuation value range
Pes: energy storage charge-discharge electric power
Ppcc: next 15 minutes dominant eigenvalues
Upper strata Optimized Operation
The first step, it is judged that the electricity of energy-storage battery, if current energy-storage battery electricity is higher than SOCup (the energy storage electricity upper limit), overcharges to ensure that battery occurs without, and needs next dispatching cycle to consider battery is discharged.If current energy-storage battery electricity is lower than SOCdown (energy storage electricity lower limit), put to ensure that battery occurred without, need next dispatching cycle to consider battery is charged.If energy-storage battery electricity is between SOCup (the energy storage electricity upper limit) and SOCdown (energy storage electricity lower limit), it is not necessary to the electricity of battery is carried out special maintenance.
Second step, as energy-storage battery need to be avoided to overcharge control, when energy-storage battery electricity is higher than SOCup (the energy storage electricity upper limit), the charge-discharge electric power Pes arranging next 15 minutes battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid, and the maximum fluctuation amount allowed according to interconnection reduces and buys electrical power from bulk power grid, i.e. the charge-discharge electric power Pes=Pload-Ppv-PpccNow+Prange of battery.
3rd step, control is put as energy-storage battery need to be avoided to cross, when energy-storage battery electricity is lower than SOCdown (energy storage electricity lower limit), the charge-discharge electric power Pes arranging next 15 minutes battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid, and the maximum fluctuation amount allowed according to interconnection increases and buys electrical power from bulk power grid, i.e. the charge-discharge electric power Pes=Pload-Ppv-PpccNow-Prange of battery.
4th step, when energy-storage battery electricity is between SOCup (the energy storage electricity upper limit) and SOCdown (energy storage electricity lower limit), the charge-discharge electric power Pes arranging next 15 minutes battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid, i.e. the charge-discharge electric power Pes=Pload-Ppv-PpccNow of battery.
5th step, battery charging and discharging limit value correction, for ensure energy-storage battery charge-discharge electric power less than PesLimit (energy storage discharge and recharge limit value), when Pes is more than PesLimit, Pes is set to PesLimit, when Pes is less than-PesLimit, Pes is set to-PesLimit.
6th step, next dominant eigenvalues instruction in 15 minutes of optimization, Ppcc (next 15 minutes dominant eigenvalues) deducts Ppv (photovoltaic prediction generated output) equal to Pload (load prediction power), then deducts Pes (energy storage charge-discharge electric power).
Compared with current dominant eigenvalues by the dominant eigenvalues instruction of the method optimization, do not have bigger fluctuation, the input that this dispatch command controls in real time as lower floor.
Lower floor controls in real time
Optimize the dominant eigenvalues instruction of module optimization according to upper strata, lower floor controls in real time.The power that target is interconnection controlled in real time fluctuates centered by Ppcc, fluctuation range is less than Prange (interconnection fluctuation range value), when photovoltaic generation prediction and load prediction deviation are less, real-time control module does not export control instruction, only when fluctuation range is more than Prange (interconnection fluctuation range value), by adjustment energy storage charge-discharge electric power, switching photovoltaic, real-time control module ensures that interconnection tie power fluctuation is not out-of-limit.
Embodiments of the invention are based on the data such as photovoltaic generation prediction, load power consumption prediction, the electricity of energy-storage battery, current dominant eigenvalues, calculate the dispatch command of following 15 minutes dominant eigenvalues, this dispatch command both can guarantee that interconnection tie power fluctuation was less than appointment scope, can effectively safeguard again the electricity of energy-storage battery, the reliability that micro-capacitance sensor is powered is ensured when bulk power grid has a power failure, the utilization rate of photovoltaic generation can also be improved, prevent and abandon optical issue, taken into account the stability of the stability of micro-capacitance sensor, the economy of micro-capacitance sensor and bulk power grid simultaneously.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (6)
1. the method that a grid type micro-capacitance sensor dominant eigenvalues is smooth, it is characterised in that: adopt dual-layer optimization scheduling；The scheduling of described dual-layer optimization includes being optimized based on prediction data the upper strata of scheduling and optimizes, and carries out lower floor's optimization of control in real time according to the real time data collected；
Described prediction data includes prediction photovoltaic generation power and prediction load generated output；Prediction data is accurate, then the result that dominant eigenvalues optimizes according to upper strata is run；Prediction data and actual data deviation are relatively big, then control optimization in real time by lower floor and carry out second-order correction.
2. the method that a kind of grid type micro-capacitance sensor dominant eigenvalues according to claim 1 is smooth, it is characterised in that: described upper strata optimizes, and defines following variable,
PpccNow: current dominant eigenvalues
Ppv: photovoltaic prediction generated output
Pload: load prediction power
SOCnow: current energy storage electricity
SOCup: the energy storage electricity upper limit
SOCdown: energy storage electricity lower limit
PesLimit: energy storage charge-discharge electric power limit value
Prange: interconnection tie power fluctuation value range
Pes: energy storage charge-discharge electric power
Ppcc: next dispatching cycle dominant eigenvalues
Its method particularly includes:
(1) electricity of current energy-storage battery is judged；
If current energy-storage battery electricity is higher than energy storage electricity upper limit SOCup, overcharge to ensure that battery occurs without, need next dispatching cycle to consider battery is discharged, and calculate the energy storage charge-discharge electric power Pes of battery；
If current energy-storage battery electricity is lower than energy storage electricity lower limit SOCdown, put to ensure that battery occurred without, need next dispatching cycle to consider battery is charged, and calculate the energy storage charge-discharge electric power Pes of battery；
If energy-storage battery electricity is between energy storage electricity upper limit SOCup and energy storage electricity lower limit SOCdown, it is not necessary to the electricity of battery is carried out special maintenance, calculates the energy storage charge-discharge electric power Pes of battery；
(2) battery charging and discharging limit value correction, for ensure energy-storage battery charge-discharge electric power less than energy storage discharge and recharge limit value PesLimit, when Pes is more than PesLimit, Pes is set to PesLimit, when Pes is less than-PesLimit, Pes is set to-PesLimit；
(3) next dominant eigenvalues instruction dispatching cycle of optimization, computing formula is:
Ppcc=Pload-Ppv-Pes.
3. the method that a kind of grid type micro-capacitance sensor dominant eigenvalues according to claim 2 is smooth, it is characterised in that in step (1), the computational methods of Pes are as follows:
(101) when energy-storage battery electricity is higher than energy storage electricity upper limit SOCup, in order to avoid energy-storage battery overcharges control, the charge-discharge electric power Pes arranging next of battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid dispatching cycle, and the maximum fluctuation amount allowed according to interconnection reduces and buys electrical power from bulk power grid, namely the charge-discharge electric power formula of battery is:
Pes=Pload-Ppv-PpccNow+Prange；
(102) when energy-storage battery electricity is lower than energy storage electricity lower limit SOCdown, control is put for avoiding energy-storage battery to cross, the charge-discharge electric power Pes arranging next of battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid dispatching cycle, and the maximum fluctuation amount allowed according to interconnection increases and buys electrical power from bulk power grid, namely the charge-discharge electric power formula of battery is:
Pes=Pload-Ppv-PpccNow-Prange；
(103) when energy-storage battery electricity is between energy storage electricity upper limit SOCup and energy storage electricity lower limit SOCdown, the charge-discharge electric power Pes arranging next of battery deducts photovoltaic generation power and interconnection output power equal to the load within microgrid dispatching cycle, and namely the charge-discharge electric power formula of battery is:
Pes=Pload-Ppv-PpccNow.
4. the method that a kind of grid type micro-capacitance sensor dominant eigenvalues according to claim 2 is smooth, it is characterised in that described lower floor optimizes, method particularly includes: optimize the dominant eigenvalues instruction obtained according to upper strata, lower floor controls in real time；The power that target is interconnection controlled in real time fluctuates centered by Ppcc, and fluctuation range is less than interconnection fluctuation range value Prange.
5. the method that a kind of grid type micro-capacitance sensor dominant eigenvalues according to claim 4 is smooth, it is characterised in that when photovoltaic generation prediction and load prediction deviation are less, real-time control does not export control instruction.
6. the method that a kind of grid type micro-capacitance sensor dominant eigenvalues according to claim 4 is smooth, it is characterized in that, when fluctuation range exceedes interconnection fluctuation range value Prange, control in real time to ensure that interconnection tie power fluctuation is not out-of-limit by adjustment energy storage charge-discharge electric power, switching photovoltaic.
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| CN113078648A (en) * | 2021-03-31 | 2021-07-06 | 安徽天能清洁能源科技有限公司 | System energy scheduling optimization method and system based on microgrid controller |
| CN113078648B (en) * | 2021-03-31 | 2024-02-09 | 安徽尚特杰电力技术有限公司 | System energy scheduling optimization method and system based on micro-grid controller |
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