WO2020097677A1 - Dispositif de commande pour un système de stockage d'énergie et de génération photovoltaïque - Google Patents

Dispositif de commande pour un système de stockage d'énergie et de génération photovoltaïque Download PDF

Info

Publication number
WO2020097677A1
WO2020097677A1 PCT/AU2019/051244 AU2019051244W WO2020097677A1 WO 2020097677 A1 WO2020097677 A1 WO 2020097677A1 AU 2019051244 W AU2019051244 W AU 2019051244W WO 2020097677 A1 WO2020097677 A1 WO 2020097677A1
Authority
WO
WIPO (PCT)
Prior art keywords
energy
battery
profile
controller
generation
Prior art date
Application number
PCT/AU2019/051244
Other languages
English (en)
Inventor
Andreas Procopiou
Kyriacos Petrou
Luis Fernando OCHOA PIZZALI
Original Assignee
The University Of Melbourne
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
Priority claimed from AU2018904310A external-priority patent/AU2018904310A0/en
Application filed by The University Of Melbourne filed Critical The University Of Melbourne
Publication of WO2020097677A1 publication Critical patent/WO2020097677A1/fr

Links

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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
    • H02S40/30—Electrical components
    • H02S40/38—Energy storage means, e.g. batteries, structurally associated with PV modules
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
    • H02J7/00714—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery charging or discharging current
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
    • H02J7/007182—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery voltage
    • H02J7/007184—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery voltage in response to battery voltage gradient
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
    • 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

Definitions

  • the present invention relates to a controller for a photovoltaic generation and energy storage system.
  • PV photovoltaic
  • BES battery energy storage
  • DNOs Distribution Network Operators
  • BES systems have to reduce their charging power rate during periods with voltage issues so that the inverter can absorb reactive power; hence, limiting the amount of energy that can be stored.
  • Described embodiments provide a control technique that mitigates against high PV exports (the cause of network issues) by adapting the BES charging power proportionally to the PV generation.
  • the power charging and discharging rates are constantly calculated throughout the day based on data indicative of clear-sky irradiance, PV generation, demand, and state of charge; significantly reducing reverse power flows and ensuring adequate storage capacity the next morning.
  • An embodiment of the invention provides a controller for an energy storage and photovoltaic generation system comprising a photovoltaic generator and a battery, the controller configured to:
  • control the battery during a first time period beginning after the photovoltaic generator stops generating energy, to gradually export energy in excess of demand to a distribution network so that the battery reaches a defined minimum stored energy by the beginning of an energy storage period;
  • control relative proportions of energy generated by the photovoltaic generator in excess of demand that are a) stored by the battery and b) exported to the distribution network during the energy storage period by: determining a desired charging profile that (i) has a gradient that follows the gradient of an estimated clear-sky photovoltaic generation profile of the photovoltaic generator, and (ii) if followed will result in the battery reaching a defined maximum stored energy by the end of the energy storage period; and
  • the controller determines the desired charging profile by modifying the estimated generation profile until the area under the curve of the estimated generation profile corresponds to the amount of energy required for the battery to reach the defined maximum stored energy from the defined minimum stored energy and setting the modified generation profile as the desired charging profile.
  • the controller periodically updates the desired charging profile based on the state of charge of the battery.
  • the estimated generation profile is a clear-sky generation profile.
  • the estimated generation profile is a forecast generation profile.
  • the controller is further configured to control the battery to supply energy during the charging period when demand exceeds photovoltaic generation.
  • the defined maximum stored energy corresponds to a full state of charge of the battery.
  • the defined minimum stored energy corresponds to an allowable minimum state of charge of the battery.
  • the first time period starts when estimated photovoltaic generation stops and ends after estimated photovoltaic generation begins.
  • the first time period ends at the beginning of the desired charging profile.
  • Another embodiment provides an inverter comprising the controller.
  • Another embodiment provides an energy storage and photovoltaic generation system comprising the controller.
  • Another embodiment provides a method of controlling an energy storage and photovoltaic generation system comprising a photovoltaic generator and a battery, method comprising to: controlling the battery, during a first time period after the photovoltaic generator stops generating energy, to gradually export energy in excess of demand to a distribution network so that the battery reaches a defined minimum stored energy by the beginning of an energy storage period;
  • determining a desired charging profile that (i) has a gradient that follows the gradient of an estimated photovoltaic generation profile of the photovoltaic generator, and (ii) if followed will result in the battery reaching a defined maximum stored energy by the end of the energy storage period;
  • the method comprises determining the desired charging profile by modifying the estimated generation profile until the area under the curve of the estimated generation profile corresponds to the amount of energy required for the battery to reach the defined maximum stored energy from the defined minimum stored energy and setting the modified generation profile as the desired charging profile.
  • the method comprises periodically updating the desired charging profile based on the state of charge of the battery.
  • the estimated generation profile is a clear-sky generation profile.
  • the estimated generation profile is a forecast generation profile.
  • the method comprises controlling the battery to supply energy during the charging period when demand exceeds photovoltaic generation.
  • the defined maximum stored energy corresponds to a full state of charge of the battery.
  • the defined minimum stored energy corresponds to an allowable minimum state of charge of the battery.
  • the first time period starts when estimated photovoltaic generation stop and ends after estimated photovoltaic generation begins.
  • the first time period ends at the beginning of the desired charging profile.
  • Another embodiment provides computer program code comprising instructions which when executed by a processor implements the above method.
  • Another embodiment provides a non-transitory computer readable medium comprising the computer program code.
  • FIG. 1A illustrates an example of desired charging and discharging profiles.
  • FIG. 1 B illustrates an example of how desired charging and discharging profiles are updated responsive to changes in conditions.
  • FIGs. 2A-2C provide comparative household level operation data for different controllers.
  • FIG. 3 is an example medium voltage feeder topology.
  • FIGs. 4A-4C illustrate comparative voltage profile performance for different controllers.
  • FIGs. 5A-5C illustrate comparative utilization level performance for different controllers.
  • FIG. 6 illustrates average household net profiles for different controllers.
  • FIG.7 contains comparative grid dependency index data for different controllers.
  • FIG. 8 illustrates an example PV generation and battery storage system.
  • FIG. 9 illustrates an example inverter.
  • FIGs. 10 to 12 illustrate further example PV generation and battery storage systems.
  • Embodiments of the invention provide a controller for systems that combine photovoltaic generation, one or more loads and battery storage.
  • Embodiments of the invention are described in relation to residential-scale systems but the controller of the embodiment can be used in other implementations where it is desirable to control energy inputs and PV exports.
  • Embodiments of the invention reduce high magnitude PV exports by adapting the BES charging power proportionally to the PV generation, and ensuring available capacity by at least partially discharging the battery overnight.
  • the controller employs an estimate of maximum PV generation.
  • the daily maximum PV generation is estimated by computing ideal clear-sky generation profiles and then, for example, storing the estimated profile in the PV device so that the PV device can retrieve it.
  • the estimated profiles can be stored elsewhere, for example in the PV inverter, or the BES inverter or the controller or any other device that can store data.
  • the profiles can also be stored somewhere remotely and accessed by the controller through communication links (i.e., wired, wireless). It is advantageous to store the profiles in the proposed controller so that access to the profiles is direct and not reliant on a communication link.
  • the estimated profile can be stored during configuration of the device, for example, by a technician downloading estimated profile data localized to the installation or by a configuration routine that contacts a server over a data network to retrieve configuration data specific to an input location.
  • the estimated profiles can be individual daily profiles for each day of the year, or one daily profile representing each month or one daily profile representing each season, etc.
  • the controller employs a desired charging profile that follows the bell-shape of the maximum PV generation with an area that matches the BES capacity, in this example, an ideal charging profile. This directly tackles reverse power flows as the BES will charge with higher power rates during critical times, reducing the amount of energy exported at these times.
  • FIG. 1A is a graph showing of active power 104 over a 24 hour time period 102 and shows an example of a desired charging profile 120 and an ideal clear-sky generation profile 1 10 to produce store a desired amount of energy, in this example 10kWh (which is the area under the curve 120).
  • Fig. 1 B illustrates that in practice, at any given time, surplus PV generation can be below that expected by the ideal charging profile.
  • PV generation and demand actual measurements
  • the embodiment uses ideal clear-sky generation profiles
  • other embodiments could use forecast data (for example, forecast radiance obtained over a data connection) or recent measured PV generation in order to obtain an estimated generation profile). Using forecasting can enhance the ability of the controller to cater for uncertainties such as cloud-effects, and changes in demand.
  • a baseline discharging power rate 130A.130B is calculated by the controller 820 taking into account the time at the end of the maximum PV generation profile, the corresponding SOC, and the time at the beginning of the charging period next day by which a defined SOC should be achieved.
  • the controller 820 controls the battery to gradually export energy in excess of demand from loads to the distribution network to ensure that the consumer makes the most of the BES system.
  • the discharging power rate is updated by the controller 820 whenever demand exceeds the baseline value. For example, following greater than average discharge of the battery in section 132A, energy is discharged more gradually.
  • FIG. 8 there is shown an embodiment of a PV generation and battery storage system 800.
  • the system 800 employs a hybrid DC coupled inverter connection where a controller 820 is imbedded within the inverter 810.
  • the inverter 810 is connected to the PV generation 830, a BES 850, and a distribution board 840.
  • the distribution board is connected to household loads 860 and the electricity grid 870.
  • a sensor 880 measures energy imported and/or exported to the grid 870 and is connected to the controller 820A via communications link 825.
  • an example inverter 810 has a DC to DC converter 814 with Maximum Power Point Tracking (MPPT) control, connected via DC links 940, 942 to PV generation 830 and BES 850.
  • the DC to DC converter 814 is connected to DC to AC converter inverter 816 which, in turn, is connected via AC link 950 to the power distribution board.
  • Both DC to DC converter 814 and DC to AC converter inverter 816 are under control of microcontroller 812.
  • controller 820A is shown as separate to microcontroller 812 in Figure 9 (and it can be implemented as a separate controller) controller 820A can also be implemented by microcontroller 812. Indeed, depending on the capabilities of existing controllers, controller 820A can be implemented by upgrading the firmware of an existing controller.
  • controller 820 can be implemented by any appropriate processing device based on computer program instructions stored in an associated memory.
  • FIG. 10 shows an alternative embodiment of a PV generation and battery storage system 1000, where instead of being embedded in the inverter, the controller is provided as an external controller 820B connected to inverter 1010 and sensor 880 via communications link 1025.
  • FIG. 11 shows an alternative embodiment of a PV generation and battery storage system 1 100.
  • a first inverter 1 15 is connected between the PV generation 830 and the distribution board 1140.
  • a second inverter is connected between distribution board 1 140 and BES 850.
  • an external controller 820C is connected to the second inverter 1 100 in order to implement the control technique.
  • External controller 820C is connected via communication link 1 125 to sensor 880.
  • FIG. 12 is a variant system 1200 on FIG.1 1 where controller 820D is embedded in inverter 1210 and connected to sensor 880 via communications link 1225.
  • the desired charging power profile is calculated by the controller 820 at each sampling interval, At (minutes). From the current instant i to the time at which PV generation ends, b, this profile is defined by a set of charging power values, C t (kW), where t E [ ⁇ , b] in steps of At. In the embodiment, this is calculated by the controller 820 iteratively reducing the corresponding clear-sky generation profile (a set of maximum PV power generation values, CS t , where t E [ ⁇ , b] in steps of At), so that the resulting area (i.e. , energy to be stored) is less or equal to the energy required to achieve full SOC, as in (1). That is, the clear-sky generation profile is adjusted
  • E s , E and h + are the rated capacity (kWh), stored energy (kWh), and the charging efficiency, respectively.
  • the clear-sky power generation profile is calculated beforehand considering the position of the Earth with respect to the Sun (changing every day, every hour) as well the characteristics of the PV installation(s) (e.g.., geographical location, panel tilt, azimuth) as shown in the example BES charging profile of Figs. 1 (a) 1 (b).
  • the clear-sky power generation profile does not need to be perfect, as the controller 820 will adjust the rate at which it charges the battery as discussed below. It will be apparent that as a result, the desired charging profile follows the gradient of the estimated clear-sky PV generation profile.
  • the charging profile tends to keep the maximum power export to the grid during any time period to a minimum, and if followed will ensure the battery will be fully charged before the end of photovoltaic generation (assuming normal operation). It also has the result of reducing the corresponding voltage at the connection point of the load, PV system and battery.
  • the desired charging power profile for a particular day and PV installation can be produced using Algorithm 1 , where n is an arbitrarily small number (a fraction of the peak clear-sky PV power generation).
  • n is an arbitrarily small number (a fraction of the peak clear-sky PV power generation).
  • the adequate definition of this number is a trade-off between n computational efficiency (larger n) and accuracy (smaller n).
  • the baseline discharging power value, D (kW), is defined, at the current instant t , to ensure that the BES system will adequately discharge at the start of the next charging period, a (time at which PV generation starts). This is given by (2).
  • (a - t) is the remaining period (minutes) until the start of the next charging period
  • rf is the discharging efficiency
  • E mm is a pre-defined minimum energy that should always be stored (manufacturer or user preference) - i.e. a base state of charge.
  • a variable definition of a and b is employed, as different times of the year, as well as different PV and BES system configurations (i.e., size, tilt, orientation etc.) result in different start and end of the charging period.
  • the start of the charging period is defined based on A2-2 and A2-3, where the reduced clear-sky profile, i.e., the ideal charging profile C t , calculated in Algorithm 1 is passed to Algorithm 2 (A2-2).
  • the value a is defined as the first period where the C t is larger than zero (i.e., BES starts to charge, shown in A2-3).
  • the end of the charging period b is calculated based on the clear-sky profile, CS t . It is defined as the last period where CS t is larger than zero (i.e., PV generation stops, shown in A2-1).
  • the daily operation of the BES system (split into a number of discrete P periods), is implemented by the controller 820 using Algorithm 3, where P and P are the demand and PV generation (kW), respectively.
  • P and P are the demand and PV generation (kW), respectively.
  • the BES system power output, Pf, and energy stored in the BES system are constrained as shown in (3) and (4), respectively.
  • P _ and P s are the minimum (discharging) and maximum (charging) power ratings (kW), respectively, and DoD is the maximum permitted depth of discharge (%).
  • Algorithm 3 is designed to maximize benefits to consumers. As such, during the charging period [a, b], any time there is a positive net demand, i - Pf > 0, the available energy stored will be used (lines A3-12, A3-13). Similarly, outside the charging period, the battery will help meeting the demand if larger than the baseline discharging power rate (A3-8, A3-9). That is energy is gradually discharged but the rate of discharge is adjusted based on actual demand.
  • Algorithm 1 Ideal Charging Power Profile
  • Algorithm 2 Defining a and b
  • A3-3 a, b ⁇ - Algorithm 2
  • A3-4 end if
  • three performance metrics are used to quantify the performance of the control approaches with respect to the network and BES owners.
  • This metric is the maximum apparent power of a transformer or current flowing in a line divided by its corresponding rated capacity (calculated at every At).
  • Percentage of consumers with voltage problems This metric takes the daily voltage profile at each consumer connection point and checks compliance with the corresponding standard.
  • this metric calculates the percentage of a household’s energy consumption that originated from the grid. This is done for the horizon of interest (e.g., a month) split into discrete Y periods.
  • the GDI value is 100%. Therefore, the lower the GDI, the more beneficial for the consumers as it leads to lower electricity bills.
  • the controller 820 is referred to as an adaptive decentralized (AD) controller 820 as the algorithm is adaptive and not subject to centralized control.
  • AD controller 820 the performance of the AD controller 820 is assessed and compared against the OTS control considering household and network level analyses. For the latter, a real Australian MV feeder with realistically modelled LV networks is considered. The yearly effects on BES owners are also quantified. For completeness, simulations are also performed for the case where consumers do not have a BES system installed (“PV only” case).
  • the distribution system analysis software package OpenDSS described in R. C. Dugan and T. E.
  • McDermott "An open source platform for collaborating on smart grid research," in Power and Energy Society General Meeting, 201 1 IEEE, 2011 , pp. 1-7, and Python are used to run the time-series, three-phase four-wire power flows as well as the control approaches.
  • the MV feeder shown in FIG. 3, is one of multiple feeders supplied by a 2x33MVA, 66kV/22kV primary substation 325.
  • the voltage at the head of the MV feeder is considered to be constant at 22kV (1 0pu) which corresponds to the voltage target setting used by the on-load tap changers (OLTCs) at the substation.
  • Each LV network is supplied by a 22kV/0.433kV distribution transformer (natural boost of 8%) with off-load tap position number 1 (reducing 5%), i.e. , effectively transforming to 411V.
  • Distribution transformers i.e., secondary transformer
  • Their rated capacities can be identified using the color map 310.
  • the total number of consumers in each LV network is assumed to be equal to the distribution transformer rated capacity divided by 4kVA (typical after diversity maximum demand for residential consumers in this area). Consequently, the total number of (single-phase connected) residential consumers in the MV feeder is estimated to be 4,626.
  • the LV networks are realistically modeled considering electrical distribution substation standards and design manuals used in Australia.
  • the resulting total number of LV feeders is 175 with a median of 1 feeder per transformer and an average main path length of 500m.
  • the total conductor length of the integrated MV-LV network amounts to 165km.
  • a pool of 30-min resolution, year-long (i.e., 17,520 points), anonymized smart meter demand data, collected from 342 individual residential customers in the year of 2014, as well as, a 30-min resolution, year-long normalized PV generation profile (also recorded in 2014) are used for the analyses.
  • the yearly-long demand and generation profiles were broken down into daily profiles, i.e., a pool of ⁇ 30,000 daily demand profiles and 90 daily PV generation profiles per season.
  • 30-min resolution, ideal clear-sky PV generation profiles are created for each day of the year using the tool developed in I. Richardson and M. Thomson. (201 1).
  • FIG. 2A presents the behavior of the household’s net demand 231 when only the PV system is installed.
  • a large amount of the household’s demand 220 is supplied by the PV generation 210. More specifically, 49% of the daily household demand is supplied by the PV system. It is important, however, to highlight that due to the high PV generation and low demand around midday, the household’s net profile results in significant exports. If multiple neighboring consumers have the same behavior, then, the resulting reverse power flows can lead to voltage rise and congestion issues in the network.
  • the OTS controller is unable to reduce the PV exports during midday (>4kW). This, as explained above, is because the BES system does not fully discharge overnight and, hence, starts charging 251 all the excess of PV generation with a relatively high SOC 241 (50% around 7:30am). Therefore, the full SOC is reached before midday. PV exports are not reduced after that point.
  • the performance of the controller 820 as well as the OTS controllers was also assessed using a real Australian integrated MV-LV network considering 100% of PV penetration (i.e., 100% of LV consumers with PV systems).
  • Real demand and PV generation data recorded on the 7th and 8th January (summer, high solar irradiance) are used.
  • the size of the PV systems is based on new installation statistics from 2016 onwards where the proportion of PV installations with 2.5, 3.5, 5.5 and 8kWp is 10, 30, 50 and 10%, respectively.
  • the storage systems used for the analysis have a capacity of 5kWp/13.5kWh (100% depth of discharge and 88% round-trip efficiency). This BES system is currently available in the Australian market and is popular with residential consumers.
  • the value of parameter n (shown in Algorithm 1) is set to be equal to 0.001.
  • the percentage of consumers with voltage problems is quantified based on the Australian Electricity Distribution Code , which states that a consumer is non-compliant if the steady-state voltage (>1 minute) exceeds the 10% of the nominal 230V line-to-neutral voltage.
  • FIGs. 4A to 4C show the daily 402 30-min voltage 404 profiles 408 of all 4,626 LV consumers for the case where households do not have BES systems installed (PV only - FIG. 4A) and the cases where BES systems are installed and controlled for both OTS controllers (FIG. 4B) and the AD controller 820 (FIG. 4C).
  • FIG. 4A shows that without a BES system, many consumers experience voltages above a statutory limit 406 (i.e., 1.1 p.u.) during the peak generation period. As shown in Table I, almost a fifth of the consumers were found to be non-compliant with the voltage statutory limit.
  • a statutory limit 406 i.e., 1.1 p.u.
  • FIG. 4B shows that voltage rise issues are reduced slightly. As a result, the number of non-compliant consumers goes down to 10%.
  • the AD controller 820 FIG. 4C, succeeds in mitigating all voltage issues.
  • the daily 50 2utilization level 504 of MV lines can rise above 180%.
  • the daily maximum utilization level goes up to 125 and 110%, respectively.
  • the metric GDI (section III. C) is calculated for a whole year for each of the 4,626 consumers. This allows to capture changes due to the seasonality and demand behavior.
  • the GDI is statistically presented in FIG. 7 for each season, using a boxplot.
  • the GDIs for the other two cases (PV only, OTS) are also quantified and presented. Considering that the OTS control is currently available to consumers and is designed for the sole benefit of the consumer, results are compared against its performance.
  • results show that the consumer’s median GDI during spring and summer (i.e. , sunny, high irradiance days) is 57 and 49%, respectively. On the other hand, during autumn and winter, where cloudy and low irradiance days exist, the GDI increases to 63 and 73%, respectively. These results, demonstrate that only using PV systems consumers can potentially achieve an annual median GDI of 60%, i.e., a 40% reduction on their annual electricity bill.
  • the proposed AD controller 820 not only provides significant benefits to the network (as discussed in the previous section) but also significantly reduces consumer grid imports. Indeed, as shown in FIG. 7the proposed AD controller 820 brings almost as much benefits to consumers as the benchmark OTS, resulting in annual median GDI of 14%, i.e., a reduction of 86% on electricity bills. It was found that this slightly higher GDI, compared with the OTS, is because of sunny days followed by a cloudy day. In such cases, once the PV generation stops in the sunny day, the AD controller 820 will fully discharge the battery by the morning of the next day (cloudy). However, due to the cloudy day (low PV generation), the BES system will not adequately charge, hence will not be able to support the local demand on that day.
  • Embodiments of the invention provide a controller 820 that implements an adaptive decentralized (AD) control strategy for residential-scale BES systems to reduce voltage and thermal issues whilst still benefiting consumers.
  • AD adaptive decentralized
  • AD controller 820 overcomes the limitations of the OTS control and allows mitigating all voltage and thermal issues. In terms of the benefits to consumers, the AD controller 820 was found to achieve almost the same performance of the benchmark OTS.
  • regulators and/or DNOs could require residential BES system manufacturers to incorporate such an algorithm in a similar way that certain PV inverter functions (e.g., Volt-Watt) are required in some parts of the world.
  • PV inverter functions e.g., Volt-Watt
  • program code could be supplied in a number of ways, for example on a tangible computer readable storage medium, such as a disc or a memory (for example, that could replace part of memory of an existing controller) or as a data signal (for example, by transmitting it from a server).
  • program code provides a series of instructions executable by a processor.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

Dispositif de commande pour un système de stockage d'énergie et de génération photovoltaïque comprenant un générateur photovoltaïque et une batterie. Pendant une première période de temps commençant après que le générateur photovoltaïque s'est arrêté de générer de l'énergie, pour exporter progressivement l'énergie en excès de demande vers un réseau de distribution de telle sorte que la batterie atteint une énergie stockée minimale définie d'ici le début de la période de stockage d'énergie ; commander des proportions relatives d'énergie générée par le générateur photovoltaïque en excès de demande qui sont stockées par la batterie et exportées vers le réseau de distribution pendant la période de stockage d'énergie en déterminant un profil de charge souhaité qui présente un gradient qui suit le gradient d'un profil de génération photovoltaïque par ciel dégagé estimé du générateur photovoltaïque, et, si cela est suivi, la batterie atteindra une énergie stockée maximale définie d'ici la fin de la période de stockage d'énergie, et commandera le stockage d'énergie dans la batterie en fonction du profil de charge souhaité.
PCT/AU2019/051244 2018-11-13 2019-11-13 Dispositif de commande pour un système de stockage d'énergie et de génération photovoltaïque WO2020097677A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
AU2018904310 2018-11-13
AU2018904310A AU2018904310A0 (en) 2018-11-13 A controller for a photovoltaic generation and energy storage system
AU2018904434A AU2018904434A0 (en) 2018-11-21 A controller for a photovoltaic generation and energy storage system
AU2018904434 2018-11-21

Publications (1)

Publication Number Publication Date
WO2020097677A1 true WO2020097677A1 (fr) 2020-05-22

Family

ID=70730964

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2019/051244 WO2020097677A1 (fr) 2018-11-13 2019-11-13 Dispositif de commande pour un système de stockage d'énergie et de génération photovoltaïque

Country Status (1)

Country Link
WO (1) WO2020097677A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112417640A (zh) * 2020-09-15 2021-02-26 国网浙江省电力有限公司湖州供电公司 一种含储能的馈线可开放容量评估方法
CN112821451A (zh) * 2021-01-11 2021-05-18 国网福建省电力有限公司泉州供电公司 一种基于需求侧管理与储能的智慧城镇配电网分布式光伏接入应对方法
WO2022089995A1 (fr) * 2020-10-27 2022-05-05 Siemens Aktiengesellschaft Appareil de stockage d'énergie et/ou de production d'énergie, ayant un dispositif de commande, et procédé de fonctionnement du dispositif de commande
CN114693095A (zh) * 2022-03-21 2022-07-01 国网湖北省电力有限公司电力科学研究院 一种应用于县域电网的分布式储能电站优化配置方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009284586A (ja) * 2008-05-20 2009-12-03 Nippon Telegr & Teleph Corp <Ntt> 電力システムおよびその制御方法

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009284586A (ja) * 2008-05-20 2009-12-03 Nippon Telegr & Teleph Corp <Ntt> 電力システムおよびその制御方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
M. J. E. ALAM ET AL.: "Mitigation of rooftop solar PV impacts and evening peak support by managing available capacity of distributed energy storage systems", IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 28, no. 4, 2013, pages 3874 - 3884, XP011530872, DOI: 10.1109/TPWRS.2013.2259269 *
SHOHANA RAHMAN DEEBA: "Development of Control Methodologies for Energy Storage Systems in Electricity Distribution Networks", PHD THESIS, 2017 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112417640A (zh) * 2020-09-15 2021-02-26 国网浙江省电力有限公司湖州供电公司 一种含储能的馈线可开放容量评估方法
CN112417640B (zh) * 2020-09-15 2024-03-29 国网浙江省电力有限公司湖州供电公司 一种含储能的馈线可开放容量评估方法
WO2022089995A1 (fr) * 2020-10-27 2022-05-05 Siemens Aktiengesellschaft Appareil de stockage d'énergie et/ou de production d'énergie, ayant un dispositif de commande, et procédé de fonctionnement du dispositif de commande
CN112821451A (zh) * 2021-01-11 2021-05-18 国网福建省电力有限公司泉州供电公司 一种基于需求侧管理与储能的智慧城镇配电网分布式光伏接入应对方法
CN114693095A (zh) * 2022-03-21 2022-07-01 国网湖北省电力有限公司电力科学研究院 一种应用于县域电网的分布式储能电站优化配置方法
CN114693095B (zh) * 2022-03-21 2024-05-31 国网湖北省电力有限公司电力科学研究院 一种应用于县域电网的分布式储能电站优化配置方法

Similar Documents

Publication Publication Date Title
Procopiou et al. Adaptive decentralized control of residential storage in PV-rich MV–LV networks
Kabir et al. Coordinated control of grid-connected photovoltaic reactive power and battery energy storage systems to improve the voltage profile of a residential distribution feeder
Hashemi et al. Methods and strategies for overvoltage prevention in low voltage distribution systems with PV
WO2020097677A1 (fr) Dispositif de commande pour un système de stockage d&#39;énergie et de génération photovoltaïque
Alam et al. Mitigation of rooftop solar PV impacts and evening peak support by managing available capacity of distributed energy storage systems
US9343926B2 (en) Power controller
EP2798717A2 (fr) Adaptation d&#39;une capacité de génération d&#39;énergie et détermination de la taille d&#39;une unité de stockage d&#39;énergie
KR20130074046A (ko) 에너지 저장장치의 충방전 제어 방법 및 시스템
CN112699562B (zh) 一种构建配电网架构的方法及终端
Marra et al. Decentralized energy storage in residential feeders with photovoltaics
KR20190112441A (ko) 에너지 관리 시스템과 그의 에너지 관리 방법 및 에너지 운영 시스템
Ju et al. Energy management system for microgrids including batteries with degradation costs
S Okundamiya et al. Techno-economic analysis of a grid-connected hybrid energy system for developing regions
JP2023138478A (ja) 高い動的負荷を有する電力システムのバッテリエネルギー貯蔵システムを制御する方法
Kaczorowska et al. Application of PSO algorithms for VPP operation optimization
CN105098810A (zh) 自适应式微网储能系统能量优化管理方法
Zupančič et al. Advanced peak shaving control strategies for battery storage operation in low voltage distribution network
CN107910866B (zh) 一种考虑需求侧响应不确定性的电力系统日前优化调度方法
Procopiou Active management of PV-Rich low voltage networks
Degefa et al. Seasonally variant deployment of electric battery storage systems in active distribution networks
De Lara Filho et al. Day-ahead robust operation planning of microgrids under uncertainties considering DERs and demand response
Rongali et al. iPlug: Decentralised dispatch of distributed generation
Badugu et al. Recharging methods of electric vehicles in residential distribution systems
Braik et al. Impact of photovoltaic grid-tied systems on national grid power factor in Palestine.
Petrou et al. Residential battery controller for solar PV impact mitigation: A practical and customer-friendly approach

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19884707

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19884707

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 19884707

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 23/11/2021)

122 Ep: pct application non-entry in european phase

Ref document number: 19884707

Country of ref document: EP

Kind code of ref document: A1