CN108695871B - Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring - Google Patents

Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring Download PDF

Info

Publication number
CN108695871B
CN108695871B CN201810412885.4A CN201810412885A CN108695871B CN 108695871 B CN108695871 B CN 108695871B CN 201810412885 A CN201810412885 A CN 201810412885A CN 108695871 B CN108695871 B CN 108695871B
Authority
CN
China
Prior art keywords
energy storage
power
storage system
energy
soc
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
Application number
CN201810412885.4A
Other languages
Chinese (zh)
Other versions
CN108695871A (en
Inventor
汪可友
赵志宇
李国杰
江秀臣
江剑峰
陈金涛
甄昊涵
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Jiaotong University
State Grid Corp of China SGCC
State Grid Shanghai Electric Power Co Ltd
Original Assignee
Shanghai Jiaotong University
State Grid Corp of China SGCC
State Grid Shanghai Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shanghai Jiaotong University, State Grid Corp of China SGCC, State Grid Shanghai Electric Power Co Ltd filed Critical Shanghai Jiaotong University
Priority to CN201810412885.4A priority Critical patent/CN108695871B/en
Publication of CN108695871A publication Critical patent/CN108695871A/en
Application granted granted Critical
Publication of CN108695871B publication Critical patent/CN108695871B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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]
    • 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
    • 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
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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
    • 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

Abstract

A configuration method for reducing the requirement on energy storage capacity of an island micro-grid containing a power spring is disclosed, wherein the island micro-grid comprises a conventional distributed power supply, an energy storage system, a diesel generator, an alternating current bus, a power load and the power spring. Conventional distributed power sources include wind power and photovoltaic. The electric load can be divided into a critical load and a non-critical load according to different requirements of the electric appliance on the quality of electric energy. The invention realizes the active interaction of the power spring and the energy storage system, can effectively reduce the charging and discharging current and power of the energy storage system, and improves the service life of the energy storage system; the demand of the system on energy storage capacity configuration is reduced, and the economy of the island micro-grid is improved. The equivalent power spring provides a part of virtual energy storage capacity for the microgrid.

Description

Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring
Technical Field
The invention relates to a micro-grid energy storage system, in particular to a configuration method for reducing energy storage capacity requirements of an island micro-grid containing power springs.
Background
In the conventional power system operation mode, the amount of power generation is determined by the load demand. With the continuous improvement of the permeability of the distributed renewable energy in the power grid, the output of the wind power and the photovoltaic new energy has randomness and volatility, the generated energy is difficult to predict accurately in real time, and larger pressure is brought to frequency modulation and voltage regulation for realizing real-time power balance between the load demand and the generated energy, so that voltage fluctuation and frequency flicker are easy to occur. The large power grid has certain self-healing capacity to voltage fluctuation, and the small island micro-grid has poor regulation capacity. It is one approach to utilize multiple energy storage devices to offset the difference between the power generation and load demand. However, energy storage systems such as chemical batteries are expensive and costly, and discarded batteries are one source of contaminants.
The operating mode of the microgrid is transitioning to "power generation determining load demand". Research on demand side energy management has emerged, including load scheduling, time of use electricity prices, and the like. However, these methods are mostly suitable for small time interval load demand management, and not for real-time energy balancing. The 'power spring' technology is used for load side energy management of a large amount of new energy accessed to a power grid, instantly transmits energy fluctuation to a non-critical load, automatically adjusts the power consumption of the non-critical load, and automatically balances generated energy and power consumption. The ensemble of power springs and non-critical loads in series is generally referred to as a smart load.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a configuration method for reducing the energy storage capacity requirement of an island micro-grid containing a power spring. The method has the advantages that the micro-grid meeting a certain scene is subjected to quantitative analysis on the minimum energy storage capacity configuration, and the minimum energy storage capacity configuration meeting the certain scene can be quantitatively determined by the provided double-layer energy optimization algorithm of the island micro-grid containing the power spring. The coordinated operation of the power spring and the energy storage system can reduce the capacity configuration of the energy storage system to reduce the investment cost. The power spring can realize energy buffering at lower cost, and the island micro-grid can operate in a new mode of tracking the generated energy by using the electricity. The excellent characteristic of the energy buffering of the power spring provides a new idea for planning and constructing a future micro-grid.
In order to achieve the above purpose, the technical solution of the invention is as follows:
a configuration method for reducing the energy storage capacity requirement of an island micro-grid containing a power spring is characterized in that the island micro-grid comprises a conventional distributed power supply, an energy storage system, a diesel generator, an alternating current bus, a power load and a power spring, the conventional distributed power supply comprises wind power and photovoltaic, the power load is divided into a critical load and a non-critical load according to the requirement of electrical appliances on the power quality, the whole of the power spring and the non-critical load which are connected in series is generally called as an intelligent load, and the method comprises the following steps:
1) the method comprises the following steps of fully investigating local loads, particularly renewable energy sources of non-critical loads, wind power and photovoltaic, and determining an optimal energy storage capacity configuration based on a simulation result of historical data, wherein a core device of a power spring comprises an inverter, the capacity configuration of the inverter is determined by the capacity of the non-critical loads, and the capacity configuration of the inverter at least exceeds 20% of the peak capacity of the non-critical loads, so that a margin is reserved;
2) first-layer energy optimization, namely global energy optimization of the island microgrid:
the first layer of energy optimization is similar to the traditional microgrid energy optimization, and the optimized objective function is the minimum function of the unit power generation price of the diesel generator:
Figure GDA0003026887410000021
wherein N represents the number of scheduling intervals within one day, λ and DtThe unit generating price of the diesel generator and the output in the t dispatching interval are respectively; considering the power constraint, capacity constraint, real scene and the like of the microgrid, the operation constraint is as follows:
0≤Wt≤Wmax (17)
|Wt+1-Wt|≤Rwmax (18)
-Scmax≤St≤Sdmax (19)
|St+1-St|≤Rsmax (20)
0≤Dt≤Ddmax-Dr (21)
|Dt+1-Dt|≤Rdmax (22)
Wt+St+Dt＝Pcl-t+kbasePnl-t (23)
equations (2) - (3) are upper and lower limit constraints and climbing constraints of fan output, WtIs the wind power output, W, of the t-th scheduling intervalmaxIs the predicted maximum output of wind power, R, of the t-th scheduling intervalwmaxIs the upper limit of the climbing power of the fan, equations (4) to (7) are the output constraint and climbing constraint of the energy storage system and the diesel generator, and StIs the discharge current, S, of the energy storage system in the t-th scheduling intervaldmaxIs the maximum discharge current, S, of the energy storage systemcmaxIs the maximum charging current, R, of the energy storage systemsmaxIs the maximum climbing power of the energy storage system, DtIs the output of the diesel generator in the t-th dispatching interval, DdmaxIs the maximum discharge power of the diesel generator, DrIs the reserve capacity of the diesel generator to protect against extreme conditions of the islanded microgrid, generally Dr＝0.1Ddmax；RdmaxIs the maximum climbing power of the diesel generator; equation (8) is the real-time power balance, P, for different types of loads under considerationcl-tIs the power of the critical load at that time, Pnl-tPower, k, not critical loadbasePnl-tAfter the power spring is additionally arranged, the non-critical load absorbs active power. k is a radical ofbaseCan be flexibly selected, and the value is 0.81;
when the energy storage system is discharged, namely S (t) is more than or equal to 0, the state of charge (SOC) of the energy storage system is expressed as:
Figure GDA0003026887410000031
when the energy storage system is charging, i.e., S (t) ≦ 0, the state of charge (SOC) of the energy storage system is expressed as:
Figure GDA0003026887410000032
where η is the self-discharge efficiency of energy storage, ηdIs the discharge efficiency, ηcEfficiency of charging, EbIs the capacity of the energy storage system, and soc (t) is the state of charge of the t-th scheduling interval; in order for the state of charge of the energy storage system to change within a safe range:
SOCmin≤SOCt≤SOCmax (26)
therein, SOCminIs the lower limit of the state of charge of the stored energy, SOCmaxIs the upper limit of the energy storage state of charge;
in order to guarantee the next day of regulation capacity of the energy storage system, the SOC variation in a single day cannot exceed a percentage delta of the total energy storage amount:
|SOCN-SOC1|≤δ (27)
the first layer of energy optimization does not consider the action of the power spring, and the solution result can be used for determining the overall minimum economic operation cost;
3) the second layer of energy optimization, namely collaborative energy optimization based on interaction of the power spring and the energy storage system:
the second layer of energy optimization mainly focuses on the interaction between the power spring and the energy storage system, and the power spring is used for adjusting the requirements of non-critical loads, so that the charging and discharging current and power of the energy storage system are reduced at a certain voltage level; the second layer of optimization objective function is a minimum function of charge and discharge power of the energy storage system:
Figure GDA0003026887410000041
if the power spring and energy storage system cooperative mechanism is activated, the constraints of the power balancing mechanism are modified and enforced as:
S1-t+kbasePnl-t＝S2-t+ktPnl-t (29)
in the formula, S1-tAnd S2-tThe calculation results of the output of the energy storage system in the first energy optimization model, the calculation results of the output of the energy storage system in the second energy optimization model and the power regulation coefficient k of the non-critical load are respectivelytThe constraint of (2) is shown in equation (15), i.e. the operating range of the smart load:
klow≤kt≤khigh (30)
wherein k islowThe lower limit of non-critical load adjustment is 0.64-1, and the influence of the willingness of non-critical load users to accept adjustment is large. k is a radical ofhighIs the upper limit of non-critical load adjustment, and the value range is 1 to 1.25; the theoretical regulation upper limit is greatly influenced by the power factor of the non-critical load, and the smaller the power factor of the non-critical load is, the larger the theoretical regulation upper limit is.
K is as describedlowAnd khighThe optimal values of (a) are 0.8 and 1.05 respectively.
The invention has the beneficial effects that:
according to the invention, the primary scheduling instruction is generated by the first layer by taking the lowest operation cost as an optimization target through the scheduling instruction generated by a double-layer energy optimization algorithm. The second layer takes the minimum charge-discharge power as an optimization target, and updates the energy storage charge-discharge power and the active demand of the intelligent load. The charging and discharging current of the energy storage system can be effectively reduced, and the service life of the energy storage battery is prolonged; the charging and discharging power of the energy storage system is reduced, the requirement of the micro-grid on the capacity of the energy storage system is reduced, and a part of virtual energy storage is equivalently provided.
Drawings
Fig. 1 is a conventional island microgrid topology of the present invention.
FIG. 2 is a schematic diagram of a two-layer energy optimization algorithm according to the present invention.
FIG. 3 is a graph of simulation results for in-day energy optimization according to the present invention, including:
FIG. 3(a) is a prediction curve of wind power output, non-critical load and critical load for a day according to the present invention;
FIG. 3(b) is a graph of the result of the energy optimization solution of the present invention without considering the power spring;
FIG. 3(c) is a graph of energy storage states of charge for different energy storage capacities according to the present invention;
FIG. 3(d) is a graph of energy storage charging and discharging before and after optimization according to the present invention;
FIG. 3(e) is a graph of the change in SOC with and without power spring according to the present invention;
fig. 3(f) is a graph of the energy optimization solution considering the power spring according to the present invention.
Fig. 4 is wind-power output data in 4 typical scenarios used in the energy optimization validation process considering power springs according to the present invention.
Detailed Description
The following embodiments and drawings are further used to explain the technical solutions of the present invention, but should not be taken to limit the scope of the present invention. For example, in the embodiment, the energy storage system of the islanded microgrid is taken as a specific implementation object of the control strategy of the invention, but the application scope of the invention should not be limited thereby.
Based on the active adjustment capacity of the power spring to the electricity consumption of the non-key load, the invention realizes the large charge and amplification of the intelligent load relaxation energy storage system through a two-layer energy optimization algorithm, reduces the charge and discharge power, can effectively reduce the charge and discharge current and power of the energy storage system, and prolongs the service life of the energy storage system; the demand of the system on energy storage capacity configuration is reduced, and the economy of the island micro-grid is improved.
An island microgrid double-layer energy optimization algorithm with power springs is shown in fig. 2. The first layer of energy optimization is to minimize operating costs within a day, while being constrained by islanding microgrid operating limitations. Based on the scheduling instructions derived from the first layer of optimization, the second layer of energy optimization based on the energy buffering action of the power springs may be used to obtain a minimum capacity configuration that satisfies the energy storage system of the current microgrid.
1) First layer-island microgrid global energy optimization
The first layer energy optimization is similar to conventional microgrid energy optimization. The optimized objective function is the function of the minimum price per unit of electricity generated by the diesel generator:
Figure GDA0003026887410000051
wherein N represents the number of scheduling intervals within one day, λ and DtThe unit generating price of the diesel generator and the output in the t dispatching interval are respectively; considering the power constraint, capacity constraint, real scene and the like of the microgrid, the operation constraint is as follows:
0≤Wt≤Wmax (2)
|Wt+1-Wt|≤Rwmax (3)
-Scmax≤St≤Sdmax (4)
|St+1-St|≤Rsmax (5)
0≤Dt≤Ddmax-Dr (6)
|Dt+1-Dt|≤Rdmax (7)
Wt+St+Dt＝Pcl-t+kbasePnl-t (8)
equations (2) - (3) are upper and lower limit constraints and climbing constraints of fan output, WtIs the wind power output, W, of the t-th scheduling intervalmaxIs the predicted maximum output of wind power, R, of the t-th scheduling intervalwmaxIs the upper limit of the fan climbing power. Equations (4) - (7) are the energy storage system and diesel generator output constraint and climbing constraint, StIs the maximum discharge current of energy storage in the t-th scheduling interval, SdmaxIs the maximum discharge current of stored energy, ScmaxIs the maximum charging current, RsmaxIs the maximum energy-storage climbing power. DtIs the output of the diesel generator in the t-th dispatching interval, DdmaxIs the maximum discharge power of the diesel generator set, DrIs the reserve capacity of the diesel generator, is used for defending the extreme condition of the little electric wire netting of island. In general, Dr＝0.1Ddmax。RdmaxIs the maximum climbing power of the diesel generator. Equation (8) is the real-time power balancing of the different types of loads considered. Pcl-tIs the power of the critical load at that time, Pnl-tPower, k, not critical loadbaseThe active power absorbed by non-critical loads after the power spring is additionally arranged.
When the energy storage system is discharged, namely S (t) is more than or equal to 0, the state of charge (SOC) of the energy storage system is expressed as:
Figure GDA0003026887410000061
when the energy storage system is charging, i.e., S (t) ≦ 0, the state of charge (SOC) of the energy storage system is expressed as:
Figure GDA0003026887410000062
where η is the self-discharge efficiency of the energy storage system, ηdIs the discharge efficiency, ηcEfficiency of charging, EbThe capacity of the energy storage system, soc (t) is the state of charge of the tth scheduling interval, and in order that the state of charge of the energy storage system changes within a safety range:
SOCmin≤SOCt≤SOCmax (11)
therein, SOCminIs the lower limit of the state of charge of the stored energy, SOCmaxIs the upper limit of the energy storage state of charge; in order to guarantee the next day of regulation capacity of the energy storage system, the SOC variation in a single day cannot exceed a percentage delta of the total energy storage amount:
|SOCN-SOC1|≤δ (12)
the first layer of energy optimization does not consider the action of the power spring, and the solution result can be used for determining the overall minimum economic operation cost;
2) the second layer of energy optimization, namely collaborative energy optimization based on interaction of the power spring and the energy storage system:
the second layer of energy optimization mainly focuses on the interaction between the power spring and the energy storage system, and the power spring can adjust the requirements of non-critical loads, so that the charging and discharging current and power of the energy storage system are reduced at a certain voltage level. The second layer optimization objective function is that the function of the charge and discharge power of the energy storage system is minimum:
Figure GDA0003026887410000071
if the power spring and energy storage system cooperative mechanism is activated, the constraints of the power balancing mechanism are modified and enforced as:
S1-t+kbasePnl-t＝S2-t+ktPnl-t (14)
in the formula, S1-tAnd S2-tThe solving results of the output of the energy storage system in the first and second layers of energy optimization models and the power regulation coefficient k of the non-key load are respectivelytThe constraints of (2) are as follows:
klow≤kt≤khigh (15)
wherein k islowThe lower limit of non-critical load adjustment is 0.64-1, and the influence of the willingness of non-critical load users to accept adjustment is large. k is a radical ofhighIs the upper limit of non-critical load adjustment, and the value range is 1 to 1.25; the theoretical regulation upper limit is greatly influenced by the power factor of the non-critical load, and the smaller the power factor of the non-critical load is, the larger the theoretical regulation upper limit is.
In general, k islowAnd khighThe optimal values of (a) are 0.8 and 1.05 respectively.
The essence of the double-layer energy optimization algorithm applied to the microgrid with the power spring island can be summarized as a second-order cone problem, and the problem can be effectively solved by using a Cplex or Gurobi commercial solver and the like.
The island micro-grid of the embodiment is expected to be configured with 2MW of wind power, 17.3MW & h of an energy storage system and 600kW of maximum output power of a diesel generator.
The wind power output and the different types of load prediction curves are shown in fig. 3 (a). The total load of the microgrid consists of critical loads and non-critical loads. The ratio of critical load to total load is different in different scheduling intervals of the day. In order to ensure that the island microgrid continuously supplies power for the next day in a normal operation mode, a constraint condition formula (12) is needed, and the energy storage system operates in a charging-discharging-charging mode.
Under the condition of not considering the power spring, based on the constraint conditions of the predicted output and safe operation of the fan, the lowest cost is taken as an objective function, and the solution result of the traditional energy optimization can be obtained, as shown in fig. 3 (b). The energy storage system still has a capacity margin during off-peak load periods. During peak loads of 15:00 to 20:00, the energy storage system reaches maximum power output and the diesel generator is put into operation. The fan outputs maximum power as much as possible within the operating constraints.
When the capacity of the energy storage system is [0.6E ]b,Eb]When the SOC is changed within the range of (b), an SOC change curve of the energy storage system can be obtained as shown in fig. 3 (c). Solving a double-layer energy optimization algorithm to obtain that the minimum energy storage capacity meeting the current operation scene of the micro-grid system is 0.599Eb. That is, if the capacity configuration of the energy storage system ESS is maintained at 0.6EbAnd above, the operating cost of the island microgrid in one day is maintained at 4423.22 yuan.
But if the capacity of the energy storage battery is less than 0.6EbThe operating cost will rise and the efficiency will drop (since it is not necessary to first discharge the recharge when the wind power is sufficient, as in the red circle of fig. 3 (c)).
If the second layer of collaborative energy optimization algorithm based on the interaction of the energy storage system and the power spring is started (the energy storage capacity is configured to be 0.6E at the momentb) The charge and discharge power of the energy storage system is reduced and the variation range of the SOC is smaller as shown in fig. 3(d) - (e). In other words, if the operating margin of the microgrid is not taken into account, then 0.6E is currently presentbThere is an excess of energy storage capacity.
At 0.6EbThe energy storage capacity configuration and the addition of the power spring operation can obtain the solution result of energy optimization, as shown in fig. 3 (f).
Further simulation results show that when the power spring is contained, the energy storage system works as the energy storage systemThe capacity of the system is reduced to 0.553EbThe one-day operation cost of the island microgrid can still be maintained at 4423.22 yuan. That is, the power spring drops the capacity of the energy storage system by 7.8%. However, if no power spring is provided, the capacity of the energy storage system drops to 0.553EbThe operating cost of a day has risen to 5488.89 dollars, and it is therefore evident that power springs play a role in reducing the capacity allocation of energy storage systems. 0.553EbThe minimum energy storage capacity configuration with feasible solution can be realized in the energy optimization of the micro-grid under the current situation, and the final configuration value of the energy storage capacity can be obtained by considering the margin according to the value.
In order to make the result more general, the wind power output levels of 4 quarters of the year in a certain area are statistically analyzed, and the wind power output prediction typical day data under 4 typical days are estimated, as shown in fig. 4.
The energy optimization of the first two stages is respectively carried out under 4 typical scenes, and the lowest energy storage capacity of the power spring is not considered and considered under the condition of maintaining the same lowest cost under each scene can be obtained, as shown in the following table 1. The critical energy storage capacity refers to the minimum energy storage capacity that allows a feasible solution to the optimization problem.
TABLE 1 relationship table of lowest capacity configuration capability for each typical scenario
Figure GDA0003026887410000081
Compared with the configuration of the energy storage capacity before and after the power spring is assembled under 4 typical scenes, the power spring can greatly reduce the requirement of the system on the capacity of the energy storage system, reduce the investment cost of energy storage equipment and improve the total income of a power grid. This is because the energy buffering action of the power spring relieves the limitation of the upper and lower limits of the stored energy charging and discharging power.

Claims (3)

1. A configuration method for reducing the requirement of energy storage capacity of an island micro-grid containing a power spring is characterized by comprising the following steps of:
1) local loads including non-critical loads, renewable energy wind power and photovoltaic investigation are subjected to simulation results based on historical data, and optimal energy storage capacity configuration is determined, wherein a core device of the power spring comprises an inverter, the capacity configuration of the inverter is determined by the capacity of the non-critical loads, the capacity configuration of the inverter at least exceeds 20% of the peak capacity of the non-critical loads, and a margin is reserved;
2) first-layer energy optimization, namely global energy optimization of the island microgrid:
the first layer of energy optimization is similar to the traditional microgrid energy optimization, and the optimized objective function is the minimum function of the unit power generation price of the diesel generator:
Figure FDA0003026887400000011
wherein N represents the number of scheduling intervals within one day, λ and DtThe unit generating price of the diesel generator and the output in the t dispatching interval are respectively; considering the power constraints, capacity constraints and real scenarios of the microgrid, the operational constraints are as follows:
0≤Wt≤Wmax (2)
|Wt+1-Wt|≤Rwmax (3)
-Scmax≤St≤Sdmax (4)
|St+1-St|≤Rsmax (5)
0≤Dt≤Ddmax-Dr (6)
|Dt+1-Dt|≤Rdmax (7)
Wt+St+Dt＝Pcl-t+kbasePnl-t (8)
equations (2) - (3) are upper and lower limit constraints and climbing constraints of fan output, WtIs the wind power output, W, of the t-th scheduling intervalmaxIs the predicted maximum output of wind power, R, of the t-th scheduling intervalwmaxIs the upper limit of the climbing power of the fan, equations (4) to (7) are the output constraint and climbing constraint of the energy storage system and the diesel generator, and StIs the discharge current, S, of the energy storage system in the t-th scheduling intervaldmaxIs the maximum discharge current, S, of the energy storage systemcmaxIs the maximum charging current, R, of the energy storage systemsmaxIs the maximum climbing power of the energy storage system, DtIs the output of the diesel generator in the t-th dispatching interval, DdmaxIs the maximum discharge power of the diesel generator, DrThe standby capacity of the diesel generator is used for resisting the extreme condition of an island microgrid; rdmaxIs the maximum climbing power of the diesel generator; pcl-tIs the power of the critical load at that time, Pnl-tPower, k, not critical loadbasePnl-tAfter the power spring is additionally arranged, the non-critical load absorbs active power;
when the energy storage system is discharged, namely S (t) is more than or equal to 0, the state of charge (SOC) of the energy storage system is expressed as:
Figure FDA0003026887400000021
when the energy storage system is charging, i.e., S (t) ≦ 0, the state of charge (SOC) of the energy storage system is expressed as:
Figure FDA0003026887400000022
where η is the self-discharge efficiency of energy storage, ηdIs the discharge efficiency, ηcEfficiency of charging, EbIs the capacity of the energy storage system, and soc (t) is the state of charge of the t-th scheduling interval; in order for the state of charge of the energy storage system to change within a safe range:
SOCmin≤SOC(t)≤SOCmax (11)
therein, SOCminIs the lower limit of the state of charge of the stored energy, SOCmaxIs the upper limit of the energy storage state of charge;
the SOC variation per day cannot exceed a percentage δ of the total amount of stored energy:
|SOCN-SOC1|≤δ (12)
the first layer of energy optimization does not consider the action of the power spring, and the solution result can be used for determining the overall minimum economic operation cost;
3) the second layer of energy optimization, namely collaborative energy optimization based on interaction of the power spring and the energy storage system:
on a certain voltage level, reducing the charge and discharge current and power of the energy storage system, wherein the second layer of optimization objective function is the minimum function of the charge and discharge power of the energy storage system:
Figure FDA0003026887400000023
if the power spring and energy storage system cooperative mechanism is activated, the constraints of the power balancing mechanism are modified and enforced as:
S1-t+kbasePnl-t＝S2-t+ktPnl-t (14)
in the formula, S1-tAnd S2-tThe calculation results of the output of the energy storage system in the first energy optimization model, the calculation results of the output of the energy storage system in the second energy optimization model and the power regulation coefficient k of the non-critical load are respectivelytThe constraint of (2) is shown in equation (15), i.e. the operating range of the smart load:
klow≤kt≤khigh (15)
wherein k islowThe lower limit of non-critical load adjustment is 0.64-1, and the influence of the willingness of non-critical load users to accept adjustment is large; k is a radical ofhighIs the upper limit of non-critical load adjustment, and the value range is 1 to 1.25; the theoretical regulation upper limit of the power factor is greatly influenced by the power factor of non-critical loadThe lower the load power factor, the larger the theoretical upper regulation limit.
2. The method according to claim 1, wherein the k is klowAnd khighThe values of (A) are 0.8 and 1.05 respectively.
3. The method for configuring an island microgrid with power springs for reducing energy storage capacity requirement of claim 1, characterized in that Dr＝0.1Ddmax。
CN201810412885.4A 2018-05-03 2018-05-03 Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring Active CN108695871B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810412885.4A CN108695871B (en) 2018-05-03 2018-05-03 Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810412885.4A CN108695871B (en) 2018-05-03 2018-05-03 Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring

Publications (2)

Publication Number Publication Date
CN108695871A CN108695871A (en) 2018-10-23
CN108695871B true CN108695871B (en) 2021-07-06

Family

ID=63845888

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810412885.4A Active CN108695871B (en) 2018-05-03 2018-05-03 Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring

Country Status (1)

Country Link
CN (1) CN108695871B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109193654B (en) * 2018-11-01 2020-12-18 珠海格力电器股份有限公司 Electric power peak shaving method, device and system
CN110048405B (en) * 2019-04-03 2023-03-28 上海交通大学 Microgrid energy optimization method based on power spring
CN110098623B (en) * 2019-04-29 2020-08-25 南京师范大学 Prosumer unit control method based on intelligent load
CN112417643B (en) * 2020-10-13 2023-05-30 国网山东省电力公司电力科学研究院 Thermal power generating unit maximum output real-time evaluation method and system based on blower current
CN113872242A (en) * 2021-10-26 2021-12-31 华北电力科学研究院有限责任公司 Active power distribution network energy optimization method and device adopting power spring
CN116689464A (en) * 2023-07-27 2023-09-05 广州汇锦能效科技有限公司 Distributed garbage treatment and energy supply system suitable for islands

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102104251A (en) * 2011-02-24 2011-06-22 浙江大学 Microgrid real-time energy optimizing and scheduling method in parallel running mode
CN105870949A (en) * 2016-04-08 2016-08-17 苏州泛能电力科技有限公司 Distributed type gradient algorithm based microgrid energy storage unit optimization control method
CN107086786A (en) * 2017-04-11 2017-08-22 天津大学 The interactive voltage-stabilizing system and operating method of bi-directional energy flow
CN107968411A (en) * 2017-11-10 2018-04-27 中国电力科学研究院有限公司 The voltage control method and device of critical loads in a kind of micro-capacitance sensor

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9733623B2 (en) * 2013-07-31 2017-08-15 Abb Research Ltd. Microgrid energy management system and method for controlling operation of a microgrid

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102104251A (en) * 2011-02-24 2011-06-22 浙江大学 Microgrid real-time energy optimizing and scheduling method in parallel running mode
CN105870949A (en) * 2016-04-08 2016-08-17 苏州泛能电力科技有限公司 Distributed type gradient algorithm based microgrid energy storage unit optimization control method
CN107086786A (en) * 2017-04-11 2017-08-22 天津大学 The interactive voltage-stabilizing system and operating method of bi-directional energy flow
CN107968411A (en) * 2017-11-10 2018-04-27 中国电力科学研究院有限公司 The voltage control method and device of critical loads in a kind of micro-capacitance sensor

Also Published As

Publication number Publication date
CN108695871A (en) 2018-10-23

Similar Documents

Publication Publication Date Title
CN108695871B (en) Configuration method for reducing energy storage capacity requirement of island micro-grid containing power spring
Lin et al. Controls of hybrid energy storage systems in microgrids: Critical review, case study and future trends
Garcia et al. Optimal energy management system for stand-alone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic
Jiang et al. A battery energy storage system dual-layer control strategy for mitigating wind farm fluctuations
CN100380774C (en) Electric power control apparatus, power generation system and power grid system
Abadlia et al. Energy management strategy based on fuzzy logic for compound RES/ESS used in stand-alone application
Hossain et al. Design a novel controller for stability analysis of microgrid by managing controllable load using load shaving and load shifting techniques; and optimizing cost analysis for energy storage system
KR20130104771A (en) Energy storage system and control method thereof
CN103986190A (en) Wind and solar storage combining power generation system smooth control method based on power generation power curves
WO2011122681A1 (en) System-stabilizing system, power supply system, method for controlling central management device, and program for central management device
Meng et al. Energy storage auxiliary frequency modulation control strategy considering ACE and SOC of energy storage
Allahvirdizadeh et al. A comparative study of energy control strategies for a standalone PV/WT/FC hybrid renewable system
CN108988337B (en) Design method of energy storage device of micro-grid system and micro-grid system
CN110783959A (en) New forms of energy power generation system's steady state control system
JP2010259303A (en) Distributed power generation system
Sayeed et al. A novel and comprehensive mechanism for the energy management of a Hybrid Micro-grid System
Choi et al. Application of vanadium redox flow battery to grid connected microgrid energy management
Boyouk et al. Peak shaving of a grid connected-photovoltaic battery system at helmholtz institute ulm (hiu)
CN115907240B (en) Multi-type peak shaving resource planning method for power grid considering complementary mutual-aid operation characteristics
Han et al. Energy storage frequency response control considering battery aging of electric vehicle
Gangwar et al. Management of energy storage dispatch in unbalanced distribution networks using opendss
CN116544982A (en) Photovoltaic absorption and peak valley arbitrage optical storage system and control method thereof
Chang et al. A dual-layer cooperative control strategy of battery energy storage units for smoothing wind power fluctuations
CN116191505A (en) Method and device for adjusting global dynamic interaction of low-voltage platform area source charge storage and charging
Lei et al. Power optimization allocation strategy for energy storage station responding to dispatch instruction

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant