NZ621385B2 - Power apparatus - Google Patents
Power apparatus Download PDFInfo
- Publication number
- NZ621385B2 NZ621385B2 NZ621385A NZ62138512A NZ621385B2 NZ 621385 B2 NZ621385 B2 NZ 621385B2 NZ 621385 A NZ621385 A NZ 621385A NZ 62138512 A NZ62138512 A NZ 62138512A NZ 621385 B2 NZ621385 B2 NZ 621385B2
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- NZ
- New Zealand
- Prior art keywords
- electrical
- schedule
- energy storage
- storage device
- load
- Prior art date
Links
- 238000004146 energy storage Methods 0.000 claims abstract description 84
- 230000036962 time dependent Effects 0.000 claims abstract description 24
- 238000007599 discharging Methods 0.000 claims abstract description 22
- 238000003860 storage Methods 0.000 claims abstract description 13
- 239000000126 substance Substances 0.000 claims description 3
- 230000005611 electricity Effects 0.000 description 64
- 238000010586 diagram Methods 0.000 description 11
- 230000000875 corresponding Effects 0.000 description 10
- 238000000034 method Methods 0.000 description 9
- 239000002253 acid Substances 0.000 description 5
- 238000010521 absorption reaction Methods 0.000 description 4
- 230000000295 complement Effects 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000001808 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 239000003245 coal Substances 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000000737 periodic Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium Ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000002354 daily Effects 0.000 description 1
- 230000003247 decreasing Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003203 everyday Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006011 modification reaction Methods 0.000 description 1
- 230000003287 optical Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 230000002459 sustained Effects 0.000 description 1
- 230000001960 triggered Effects 0.000 description 1
- 238000004450 types of analysis Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
- H02J4/00—Circuit arrangements for mains or distribution networks not specified as ac or dc
-
- 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
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/10—Energy trading, including energy flowing from end-user application to grid
Abstract
The current invention relates to a power apparatus comprising an input connectable to a mains electrical supply (130); an energy storage device (106); a supply converter (104); a load converter (108); an output (110) and a control device (112), coupled to a communications network. The supply converter (104) selectively connects to an electrical supply (130) to convert electrical power from the electrical supply (130) to energy for storage in the energy storage device. The load converter (108) arranged to convert energy from the energy storage device (106) to electrical power for supply to an electrical load. The output (110) selectively connectable to either of the input or the load converter, which the electrical load is coupled to the apparatus to receive electrical power. The control device (112) is configured to: receive, from the communications network, time-dependent electrical pricing data associated with the mains electrical supply; determine a schedule using at least the received time-dependent electrical pricing data for each of (i) charging the energy storage device, (ii) supplying power from the input to the output (110), and (iii) discharging the energy storage device (106) to the output (110), selectively connect the supply converter (104) to the input according to the schedule; and selectively connect the output (110) to either of the input or the load converter (108) according to the schedule to provide electrical power to the electrical load. er (104) selectively connects to an electrical supply (130) to convert electrical power from the electrical supply (130) to energy for storage in the energy storage device. The load converter (108) arranged to convert energy from the energy storage device (106) to electrical power for supply to an electrical load. The output (110) selectively connectable to either of the input or the load converter, which the electrical load is coupled to the apparatus to receive electrical power. The control device (112) is configured to: receive, from the communications network, time-dependent electrical pricing data associated with the mains electrical supply; determine a schedule using at least the received time-dependent electrical pricing data for each of (i) charging the energy storage device, (ii) supplying power from the input to the output (110), and (iii) discharging the energy storage device (106) to the output (110), selectively connect the supply converter (104) to the input according to the schedule; and selectively connect the output (110) to either of the input or the load converter (108) according to the schedule to provide electrical power to the electrical load.
Description
NZ_9414795_13
POWER APPARATUS
Technical Field
The present invention relates generally to energy storage and utilization and, in
particular, to a power apparatus useful for efficient energy consumption.
Background
Electrical power supply is usually provided via a publicly accessible electricity
power network grid arranged in a hierarchy of energy suppliers, energy retailers and
energy consumers. Traditional energy suppliers operate large power plants and supply
the power they generate to energy consumers via the electrical power network grid. The
power plants may include coal fired power, wind farm, nuclear plant, geothermal, solar
farm, hydroelectric plants, and gas turbines. In order to ensure stability and
predictability of the electricity cost to the energy consumers, energy retailers purchase
the power supplied by energy suppliers in bulk and on-sell the power to energy
consumers.
Energy retailers are charged for their distribution network usage according to a
cost reflective network price. Cost reflective network pricing requires off-peak prices to
be low to reflect the near zero marginal cost of distributing electrical energy during off-
peak times, and peak prices to be high to reflect the Long Run Marginal Cost (LRMC)
of expanding the energy network to distribute additional electricity.
The increased usage of renewable energy has impacted upon the power
network. This increases the unpredictability of electricity demand from energy
consumers, which impacts upon the electricity spot pricing (i.e., the real-time prices of
electricity paid by energy retailers). The unpredictability of the electricity spot pricing
further impacts the profitability of energy retailers. In Australia, the electricity spot
price paid by retailers to suppliers can fluctuate between (minus)$2/kWh to
(plus)$12,50/kWh, whilst consumers may typically pay between $0.12/kWh to
$0.40/kWh.
995717NZ_9414795_13
Summary
According to a first aspect of the present disclosure, there is provided a power
apparatus comprising: an input connectable to a mains electrical supply; an energy
storage device; a supply converter selectively connectable to an electrical supply to
convert electrical power from the electrical supply to energy for storage in the energy
storage device; a load converter arranged to convert energy from the energy storage
device to electrical power for supply to an electrical load; an output, selectively
connectable to either of the input or the load converter, by which the electrical load is
coupled to the apparatus to receive electrical power; and a control device, coupled to a
communications network, configured to: receive, from the communications network,
time-dependent electrical pricing data associated with the mains electrical supply;
determine a schedule, using at least the received time-dependent electrical pricing data,
for each of (i) charging the energy storage device, (ii) supplying electrical power from
the input to the output, and (iii) discharging the energy storage device to the output;
selectively connect the supply converter to the input according to the schedule; and
selectively connect the output to either of the input or the load converter according to
the schedule to provide electrical power to the electrical load.
According to another aspect of the present disclosure, there is provided a
system comprising at least one power apparatus, a communications network, and a
server computer device, said power apparatus comprising: an input connectable to a
mains electrical supply; an energy storage device; a supply converter selectively
connectable to an electrical supply to convert electrical power from the electrical supply
to energy for storage in the energy storage device; a load converter arranged to convert
energy from the energy storage device to electrical power for supply to an electrical
load; an output, selectively connectable to either of the input or the load converter, by
which the electrical load is coupled to the apparatus to receive electrical power; and a
control device, coupled to the communications network, configured to receive a
schedule from the server computer device by which the control device selectively
connects the supply converter to the input and selectively connects the output to either
of the input or the load converter according to the received schedule; and the server
computer device is coupled to the communications network and is configured to:
receive, from the communications network, time-dependent electrical pricing data
associated with the mains electrical supply; determine the schedule for the power
995717NZ_9414795_13
apparatus for each of (i) charging the energy storage device, (ii) supplying power from
the input to the output, and (iii) discharging the energy storage device to the output, and
send the determined schedule to the control device.
According to another aspect of the present disclosure, there is provided an
application program, executable by a computerized processor for determining a
schedule for an operation of a power apparatus, the power apparatus being configured to
provide electrical power to an electrical load, the power apparatus comprising: an
input connectable to a mains electrical supply; an energy storage device; a supply
converter selectively connectable to an electrical supply to convert electrical power
from the electrical supply to energy for storage in the energy storage device; a load
converter arranged to convert energy from the energy storage device to electrical power
for supply to an electrical load; an output, selectively connectable to either of the input
or the load converter, by which the electrical load is coupled to the apparatus to receive
electrical power; and a control apparatus configured for: selectively connecting the
supply converter to the input according to the schedule, and selectively connecting
the output to either of the input or the load converter according to the schedule to
provide electrical power to the electrical load; and the application program comprising:
code for receiving, from a communications network, time-dependent electrical pricing
data associated with the mains electrical supply; code for determining a load forecast
based on historical electrical consumption data of the electrical load or a standard
profile of the type of electrical load; code for determining a schedule for discharging the
energy storage device to the electrical load based on the determined load forecast,
discharge cost of the energy storage device, and the received time-dependent electrical
pricing data; and code for determining a schedule for charging the energy storage device
based on the discharge schedule, a recharge profile of the energy storage device and the
received time-dependent electrical pricing data.
Brief Description of the Drawings
At least one embodiment of the present invention will now be described with
reference to the drawings, in which:
Fig. 1 shows a power apparatus upon which arrangements described can be
practised;
995717NZ_9414795_13
Fig. 2 shows the controller of Fig. 1;
Fig. 3A shows how multiple power apparatus may be used in an electricity
system;
Fig. 3B shows how multiple power apparatus may be controlled or aided in
operation by a server in an electricity system;
Fig. 4 depicts a software architecture for the power apparatus;
Fig. 5 is a flow diagram of the interconnections of the various application
programs of Fig. 4;
Fig. 6 is a flow diagram to develop a schedule and updating of the schedule of
a power apparatus for a normal operational day;
Fig. 7 is a flow diagram for a method for determining a discharge schedule of
the power apparatus;
Fig. 8 is an example of electricity forecast prices based on reliability pricing
used in determining schedule of Fig. 7;
Fig. 9 is an example of electricity forecast prices based on network pricing
used in determining schedule of Fig. 7;
Fig. 10 is an example of electricity forecast prices based on wholesale pricing
used in determining schedule of Fig. 7;
Fig. 11 is an example of a load forecast used in determining discharge schedule
of Fig. 7;
Fig. 12 is an example of a loss curve of a lead-acid battery;
Fig. 13 is an example of a discharge schedule and a forecast daily profit
generated from the method of Fig. 7;
Fig. 14 is a flow diagram for a method for determining a schedule for charging
of the power apparatus;
Fig. 15 is an example of battery charging stages;
Fig. 16 is an example of a charge schedule and a forecast energy charging cost
from the method of Fig. 14; and
Fig. 17 is an example of a charging and discharging schedule;
Fig. 18 is a flow diagram for an interrupt method for determining an optimal
schedule of a power apparatus when an increase in an electricity price occurs.
995717NZ_9414795_13
Detailed Description
The present disclosure relates to a power apparatus operable to store and to
supply power so as to minimise costs incurred for connected loads. The power
apparatus minimises costs by storing electrical power into an energy storage device
when the electricity price is relatively low and by supplying the stored electrical power
to the electrical load when the electricity price is relatively high. The power apparatus
manages the storing and supplying of electrical power based upon the relative costs of
using stored and mains energy. Other factors such as forecasted wholesale electricity
prices, weather, and any available network and retail supply tariffs may also be
considered to optimise scheduling of storing of the electrical power to the power
apparatus and supplying of the electrical power to a connected load by the power
apparatus. The power apparatus may be transportable or in a fixed configuration at a
premises.
Fig. 1 shows a power apparatus (PA) 100 including an enclosure 101 having an
input 102 for coupling to a mains electrical power supply 130, and an output 110 for
providing electrical power to an electrical load 132. The PA 100 has a supply converter
104 for converting electrical power from the mains supply 130 to a form suitable for
storage in an energy storage device 106. The PA 100 has a load converter 108 for
converting the energy stored in the energy storage device 106 to electrical power for
supply to the electrical load 132. The electrical load 132 may be an appliance such as a
refrigerator, an oven, an air conditioner, a computer, an electric vehicle, a coffee
machine or any other device that requires electricity for operation. The PA 100 may
also include an alternative energy input 118 which may be generated from, inter alia,
local solar panels, local wind turbines, local hydroelectricity, local generators, etc.
The output 110 is typically a power socket of the same configuration of the
mains electrical power supply 130. An electrical load 132 can typically connect to the
output 110 with a standard mains electrical supply complementary plug.
An arrangement of switches S1, S2, and S3, selectably switchable by a
controller 112 of the PA 100, provide for the charging of the energy storage device 106
and the supply of electrical energy to the output 110 for powering the load 132. Switch
S1 for example is closed when costs for the mains supply 130 are relatively low to
thereby provide for storing energy in the energy storage device 106. Switches S2 and
S3 are ganged for complementary operation to selectively couple the output 110 to one
995717NZ_9414795_13
of the input 102, for supply from the mains supply 130, or to the load converter 108, for
supply from the energy storage device 106. Typically S2 is closed and S3 is open when
mains supply 130 costs are relatively low, and S2 is open and S3 is closed when the
mains supply 130 costs are relatively high. Whilst Fig. 1 illustrates S2 and S3 as a
complementary operating double-pole-double-throw switch, such may be implemented
by a single-pole-double-throw switch.
The controller 112 controls selectable switches S1, S2, S3 via control signals
transmitted via connections 119, 121.
In a typical and preferred implementation, the energy storage device 106 is a
chemical battery (e.g., a lead acid battery, a lithium ion battery) and the converter 104 is
a rectifier and a charger unit configured to rectify an AC mains supply 103 to DC for
charging the battery 106. In an alternative embodiment, the converter 104 is configured
to rectify AC power supply from the alternative energy input 118 to DC for charging the
battery 106. In yet another alternative embodiment, the alternative energy input 118
may output DC power to directly charge the battery 106.
The load converter 108 is preferably an inverter configured to convert the
battery voltage to a AC supply for the load 132, essentially mirroring the mains supply
130.
Sensors 113 are provided to measure supply voltage via connection 123,
battery voltage via connection 125, battery temperature via connection 127, and load
current via connection 131. A phase control connection 129 may be provided between
the input 102 and the load converter 108 to ensure phase synchronisation between the
two, as adjusted by operation of the load converter 108. Data from sensors 113 is
transmitted to controller 112 via connection 117. The controller 112 processes the data
from sensors 113 to execute a predetermined action based on the received data. The
predetermined action is discussed in detail below in relation to Figs. 4 and 5.
The controller 112 is associated with a memory 114, which stores a schedule of
operation for the PA 100 to store and to supply electrical power, data from sensors 113
and any other application programs to operate the PA 100. Memory 114 is coupled to
controller apparatus 112 via a connection 133. Controller 112 may also be connected to
a communications interface 116, by which PA 100 is configured to communicate with a
communications network 140. Communications network 140 may be a local area
network (LAN), or a wide area network (WAN) such as the Internet. The
995717NZ_9414795_13
communications network 140 may provide external data such as historical, current and
forecasted electricity network prices, market prices, retailer/supplier prices, customer
prices; forecasted electricity local demand; weather; and any other data that may impact
the electricity price of the mains electrical power supply 130. The communications
interface 116 may operate according to wired (telephone line) or wireless protocols.
The PA 100 is preferably configured as a transportable unitary device directly
connectable between a traditional general purpose outlet (GPO), representing the mains
supply 130, and the load 132, represented by an appliance as discussed above, having a
lead and plug 133 that would ordinarily connect to the GPO. The PA 100 may be
supplied for physical location with the load appliance 132 and the physical size of the
PA 100 will depend predominantly by the energy storage capacity thereof. Such size
will depend mainly upon the type of battery 106 used and the overall storage capacity.
Although typically the PA 100 would not be regarded as “hand-portable” device, the
enclosure 101 would typically be sized for relative ease of movement and positioning,
by a trolley for example (e.g., have a volume between about 1.00m – 1.50m ).
Fig. 1 also shows a (local or remote) computer 150 connected via the
communications network 140 and connection 151 to the communications interface 116,
or alternatively directly to communications interface 116 via connection 153. The
computer 150 is generally connected and operative during setup and installation to load
application programs and default settings of PA 100 to memory 114 for execution by
controller 112. Some examples of default settings of PA 100 include a reliability price
of the load 132 coupled to a PA 100, battery type, battery size and tolerance threshold
parameters of a PA 100.
Reliability price of the load 132 is typically a user-specified price that sets the
importance of maintaining power to the load 132 when mains electrical supply 130 is
lost during a power outage. Higher reliability price equates to more importance in
maintaining power to a load 132. Reliability price is further discussed in relation to Fig.
The tolerance threshold parameters are user-specified values that may establish
actual electrical price difference against the forecasted electrical price; and nominal and
maximum rates of charge, depths of discharge, and operating temperature of the battery
106. Tolerance threshold is further discussed in relation to Fig. 6.
995717NZ_9414795_13
Continued or operational connection permits the computer 150 to interact with
PA 100 to display the status of PA 100 on the display (not shown) of computer 150.
Further, sustained connection of the computer 150 allows a user to manually control the
operation of PA 100 in exceptional circumstances. For example, a user may force PA
100 to shut down, to restart, to charge or discharge energy, to be bypassed or to execute
a manually determined schedule. Typically, computer 150 only updates the default
settings of PA 100 based upon new parameters entered by a user. In another
implementation, computer 150 may also perform some of the functions of controller
112.
The controller apparatus 112 processes the received external data, in
combination with data from sensors 113, to establish an optimal schedule for storing
and supplying power by the transportable power apparatus 100.
The transportable power apparatus 100 may also include a display 126 coupled
to the controller 112. The display 126 is typically a liquid crystal display (LCD) panel
or the like that allows a user to check the status of the transportable power apparatus
100.
Fig. 2 shows a schematic block diagram of the controller 112 of the PA 100.
The controller 112 comprises a processor 214 which is bi-directionally coupled via an
interconnected bus 213 to a display interface 212, an I/O Interface 210, a portable
memory interface 211, and the memory 114.
Typically the controller 112 has an on-board memory. Memory 114 is coupled
to processor 214 as additional memory. The on-board memory of processor 214 and
memory 114 may be formed from non-volatile semi-conductor read only memory
(ROM), semi-conductor random access memory (RAM) and possibly a hard disk drive
(HDD). The RAM may be volatile, non-volatile or a combination of volatile and non-
volatile memory.
The sensors 113, discussed above, are also connected to the I/O Interface 210
for providing sensors data to processor 214.
Fig. 2 also shows that the controller 112 utilises I/O Interface 210 for coupling
to the communications interface 116, for communicating with communications network
140.
The portable memory interface 211 allows a complementary portable memory
device 215 to be coupled to the PA 100 to act as a source or destination of data.
995717NZ_9414795_13
Examples of such interfaces permit coupling with portable memory devices such as
Universal Serial Bus (USB) memory devices, Secure Digital (SD) cards, Personal
Computer Memory Card International Association (PCMIA) cards, optical disks and
magnetic disks. These portable memory devices may be used to load the application
programs and default settings of the PA 100.
The display interface 212 is connected to the display 126. The display
interface 212 is configured for displaying information on the display 126 in accordance
with instructions received from processor 214, to which the display interface 212 is
connected.
Fig. 3A shows a system including an electricity power grid 310 and the
communication network 140 within which the power apparatus 100 may be connected.
Fig. 3A depicts a decentralised system of multiple PAs 100. The electricity power
network grid 310 is connected to electricity power generators such as coal plant 320,
nuclear plant 318, hydroelectric plant 316, wind farm 314, and solar farm 312, or the
like. The grid 310 also includes transformers (not shown), substations 311 and other
structures which facilitate the supply and distribution of electrical energy from the
power plants to the energy consumers. A retailer 350, a market operator 351, or a
network operator 353 may be configured to provide a constant or periodic update on the
network, retail, and wholesale electricity prices of the electricity power network grid
310 to the communications network 140. Network, retail, and wholesale prices are
discussed below in relation to Fig. 7. System 300 also shows a plurality of PA 100a, …,
100n. The PA may be placed in businesses, houses or the like, each corresponding to
electricity consumer having an electricity meter. The PA 100a, …, 100n are connected
to the communication network 140 in order to obtain historical, current and forecasted
electricity prices supplied by any of the retailer 350, the market operator 351, or the
network operator 353. The communications network 140 may also be coupled to the
Bureau of Meteorology 324 or other appropriate source to provide data on current and
forecast weather. When the power apparatus 100 receives data from these sources, the
controller 112 processes the received data and establish an optimal schedule for
operation of the PA 100 for storing and supplying electrical power to the corresponding
electrical load 132. In a specific implementation of the system of Fig. 3A, particularly
where the PAs 100 are generally proximate and subject to the same supply availability
and pricing, the PA 100a, …, 100n may also communicate with each other via the
995717NZ_9414795_13
network 140 to determine optimal individual schedules for storing and supplying
electrical power to corresponding electrical loads 132a, …, 132n.
For example, when a group of PA 100a, …, 100n in the same substation 311
communicate with each other and establish optimal individual schedules for that
particular group, electricity demand for the particular substation may be decreased
during peak hours when network price is high and increased during off-peak hours
when network price is low, effectively saving money for the energy retailers and
provide a better load distribution for the electricity power network grid 310.
Fig. 3B depicts a centralised system of PAs 100 used in an electricity system.
A centralised server computer 350 is configured to operate a set of PA 100a, …, 100n.
The server computer 350 collates the external data from a retailer 350, a market
operator 351, or a network operator 353, and Bureau of Meteorology 324 and user-
specified data, such as reliability prices, and establishes optimal schedules of PA 100a,
…, 100n in order to minimise costs to connected loads 132a, …, 132n. The established
schedules are then communicated to the respective PAs 100, which then implement the
schedule by timely operation of the switches S1, S2 and S3.
The server computer 350 is typically a computer with a large processing power
to monitor and to establish schedules for a group of PAs 100. Similar to the controller
112, the server computer 350 includes at least a memory, a processor, I/O interfaces, a
display interface and a portable memory interface. The memory of the server computer
350 may include a database of PAs 100 that the server computer 350 is managing.
Fig. 4 is a representation of the software architecture 400 to operate the PA
100, and Fig. 5 is a flow diagram of a high level operation 500 depicting the
interconnections between the application programs of the software architecture 400.
The software architecture 400 comprises a data management application program 402,
which manages system data and collated data from external data application program
404 and sensors application program 406. System data includes battery type, battery
configuration, proprietary battery charge and discharge profiles, and battery
manufacturer specification. External data application program 404 collates data from
the communications network 140 and computer 150, whilst sensors application program
406 collects data from the sensors 113. The architecture 400 and applications programs
402-414 are stored in the memory 114 and are executable by the processor 214. The
995717NZ_9414795_13
data provided by communications network 140, computer 150 and sensors 113 have
been discussed above.
In a preferred implementation, as depicted in Fig. 5, the external data
application program 404, sensors application program 406, and data management
application program 402 collect and organise the data at predetermined intervals (e.g.,
every 24 hours) or at user-specified intervals (e.g., 5 minutes, 30 minutes, 60 minutes).
The interval of collecting data may be amended by a user from computer 150.
The software architecture 400 has an optimisation application program 408,
which processes the collated data of the data management application program 402 and
produces optimal operating schedules for PA 100. The optimisation application
program 408 also monitors for emergency situations and manual override commands
from computer 150 for altering the schedule accordingly. Typically in a manual
override situation, a user manually enters a new schedule and updates the PA 100 with
the new schedule, which the optimisation application program 408 adopts.
For example, if selectable switch S2 is closed and the mains electrical power
supply 130 loses power, the sensors application program 406 operates to detect the loss
of power and the optimisation application program 408 subsequently processes the data
and checks whether the reliability price of the load 132 is higher than the discharge cost
of the battery 106. Discharge cost of a battery 106 is the potential cost incurred in
discharging the battery to load 132. Discharge cost of the battery 106 is further
discussed below in relation to Fig. 7. If the reliability price is higher than the discharge
cost, it means it is cheaper for the user to discharge the battery 106 to load 132, than to
allow load 132 to lose power. In this case, the optimisation application program 408
alters the schedule to allow the energy storage device 106 to supply electrical power to
the electrical load 132 by effectively opening S2 and closing S3.
Typical operation of optimisation application program 408 in producing
optimal schedules and updating of the optimal schedules is discussed below in relation
to Fig. 6.
Scheduling application program 410 receives optimal schedules from the
optimisation application program 408 and maintains the schedules for charging the
energy storage device 106 and for selecting the electrical power supply for the output
110. The scheduling application program 410 includes an internal real-time clock to
track the passage of time.
995717NZ_9414795_13
Controller application program 412 interprets schedules from scheduling
application program 410 to selectively open and close switches S1, S2 and S3.
In a decentralised operation of PA 100 as depicted in Fig. 3A, communications
application program 414 transmits the collated data of data management application
program 402 and the optimal schedules produced by optimisation application program
408 to computer 150. Computer 150 subsequently displays the collated data and
optimal schedules on a display of computer 150 for a user to monitor the operation of
PA 100.
In a centralised operation of PA 100 as depicted in Fig. 3A, communications
application program 414 receives optimal schedules set by optimisation application
program 408 in computer 150 and transmits collated data from sensors application
program 406 to computer 150. Computer 150 subsequently displays the sensors data on
a display of computer 150 for a user to monitor the operating parameters of PA 100.
The methods described hereinafter is implemented using the processor 214,
where the process of Fig. 6 may be implemented as one or more software application
programs 402 to 414, shown in Fig. 4. In particular, with reference to Fig. 4, the steps
of the described methods are effected by instructions in the software that are carried out
within the processor 214. Alternatively, some of the described methods may be
implemented in the server computer 350 if PAs 100 are operated in a centralised
system. The software instructions may be formed as one or more code modules, each
for performing one or more particular tasks. The code modules are stored in a memory
and executable by either the PA 100 for a decentralised system or the server computer
350 for a centralised system.
Typically, the application programs 402 to 414 discussed above are resident on
the memory 114 and are read and controlled in their execution by the processor 214,
and in the following description, this will be assumed to be the case.
Intermediate storage of the application programs 402 to 414 and any data fetched
from the communications network 140 may be accomplished using the on-board
memory of processor 214, possibly in concert with the memory 114.
Fig. 6 is a flow diagram for a method 600 in determining an optimal schedule
of charging and discharging of PA 100 for a normal operational day and updating of the
optimal schedule upon receipt of new data and/or commands from communications
network 140 and/or computer 150. The method 600 starts at step 602, which
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corresponds to the optimisation application program 408. Step 602 determines if an
optimal schedule needs to be produced for the next day. Typically, the only time that an
optimal schedule needs to be created for the next day is at the end of a current day. If
an optimal schedule needs to be determined, step 602 moves to next step 604.
At step 604, the optimisation application program 408 determines whether
sufficient historical data is available to forecast the electricity consumption of electrical
load 132. Hereinafter, forecasts of electricity consumption of electrical load 132 will be
referred to as the load forecast.
Typically, a 24 hour period of operating history of the same day type must
have occurred before a load forecast can be determined. Day type includes weekday,
weekend and holiday by default, but may also include additional day types relevant to a
particular site. An example of relevant day types is school holidays for a business
receiving custom from a nearby school.
For example, if the PA 100 is installed on a Thursday (i.e., a weekday), there is
insufficient data to develop a load forecast for Friday (i.e., a weekday) as the PA 100
does not have a full 24 hour of a weekday data. There is also insufficient data to
develop a load forecast for Saturday (i.e., weekend) as data collated on Friday is only
for weekday. Thus, a first load forecast for weekend type is developed for the ensuing
Sunday based on collected data on the Saturday. Accordingly, a first load forecast for
weekday type is developed for the following Monday based on collected data on the
Friday. If there is insufficient data, method 600 continues to step 605.
At step 605, the optimisation application program 408 sends a signal to
communications application program 414 for notifying computer 150 that load forecast
cannot be determined. In this case, the PA 100 runs a default schedule or a schedule
that has been determined by a user.
On the other hand, method 600 advances to step 606 from step 604 if the
optimisation application program 408 determines there is sufficient data. Load forecast
is developed at step 606. The load forecast is determined from a best fit model for each
interval i (e.g., 30 minutes or a shorter user-specified interval) using the equation:
kWh = + x + x + x + x + x + (eqn. 1)
i 1 1 2 2 2 2 3 3 n n
Where:
kWh = Forecasted Load at interval i
= base electricity consumption (kWh)
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X = independent variables (e.g., weather (e.g., minimum and
1..n
maximum temperature, humidity, precipitation, wind speed), type of
day (e.g., weekday, weekend, holiday), type of week (e.g., Monday,
Tuesday, etc), type of month (e.g., May, June, July, etc), type of
season (e.g., summer, autumn, winter, spring), type of interval, etc)
= Estimated coefficient corresponding to each independent
1..n
variable, which has been calculated using a standard linear regression
method for minimising standard error term.
= Standard error term.
The base electricity consumption ( ) is determined based on historical energy
consumption data of a load 132 or a standard profile of the type of electrical load. For
example, if the load 132 is a coffee machine, the base electricity consumption ( ) may
be the same coffee machine’s historical data. Alternatively, the base electricity
consumption ( ) may be a standard profile of the electricity consumption of a
comparable coffee machine or the electricity consumption of another electrical machine
consuming electricity in a similar manner as a coffee machine.
The optimisation application program 408 tests each permutation of
independent variables (i.e., X ) and selects the permutation with the best fit, as
1..n
determined by the highest adjusted r-squared (i.e., a standard statistical measure for how
well a regression line approximates real data points). Each independent variable
coefficient (i.e., ) is estimated for each permutation using historical data of the past
1..n
one day, the past one week, the past one month and the past one year.
For example, initially the highest adjusted r-squared and associated coefficients
( ) are determined for a load forecast (forecast A) using all available independent
1..n
variables (X ). Historical data of the independent variables (X ) are utilised to
1..n 1..n
calculate the load forecast. Evaluation of eqn. 1 proceeds by removing one or more
different independent variables (X ); calculating a new load forecast (forecast B)
1..n
coefficients ( ); and determining the load forecast with the highest r-squared. The
1..n
load forecast with the higher r-squared is kept. The permutations continue until all
permutations have been tested, and the permutation with the highest r-squared is
determined.
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An example of a load forecast for a day is shown in Fig. 11. Method 600
advances to step 607.
Step 607 develops a discharge schedule for a day for the PA 100. The
discharge schedule is developed based upon minimising the cost of supplying the
connected load 132. Development of discharge schedule is discussed in relation to Fig.
Method 600 advances to step 608. At step 608, the optimisation application
program 408 develops a charge schedule for PA 100. Details for developing a charge
schedule is discussed in detail in relation to Fig. 14. The method 600 concludes when
step 608 is complete.
If at step 602 the optimisation application program 408 determines that a new
schedule does not need to be generated, the method 600 advances to step 610. At step
610, the optimisation application program 408 obtains current data from
communications network 140, computer 150 and sensors 113. The method 600
continues to step 612.
At step 612, the optimisation application program 408 determines if any
current data exceeds a forecast price, a forecast cost or any other electrical parameters
(e.g., battery depth of discharge, battery temperature) by a tolerance threshold value set
by a user. Forecast price and forecast cost are discussed in relation with Fig. 7.
For example, a user may set a tolerance threshold for battery depth of discharge
to +1% for a battery specified as having a nominal depth of discharge of 50%. If the
battery depth of discharge has exceeded the allowable threshold (i.e., above 51%), the
optimisation application program 408 may alter the schedule to effectively disconnect
the battery from mains supply 130 and load 132. A battery depth of discharge is set to
prevent the battery from being discharged beyond 50% because a depth of discharge
beyond 50% may significantly increase the discharge cost possibly exponentially.
Typically, such a battery that is regularly discharged to 50% of its full capacity
will last about 6 years. Conversely, the same battery that is regularly discharged to 90%
or above will last only about 3 years.
In another example, a user may set a tolerance threshold for a forecast price to
+$0.05/kWh. A forecast price for 10am to 11am is $0.2/kWh and the period is not a
scheduled discharge period. If the actual electricity price during that period goes above
995717NZ_9414795_13
$0.25/kWh, the optimisation application program 408 alters the schedule to discharge
the battery 106 during that period as the tolerance threshold has been exceeded.
Typically, the optimisation application program 408 monitors whether data has
exceeded a tolerance threshold in real time. If no data has exceeded the corresponding
tolerance threshold, the method 600 concludes. Otherwise, method 600 advances to
step 614.
Step 614 performs the procedure described in steps 606 to 608, and generates a
new schedule for the charging and supplying of electrical power by PA 100. Method
600 concludes after generating a new optimal schedule.
Fig. 7 is a flow diagram for a method for determining a discharging schedule of
the PA 100. The method 700 commences with step 701, which determines at least four
different forecast prices for each user-specified interval for one full day.
The four forecast prices are as follows:
- Reliability forecast price is typically based on a local consumer-specified
value of maintaining power to an electrical load 132. This value may be
amended by an authorised local consumer at any time. An example is shown
in Fig. 8.
- Network forecast price based on a smart meter tariff set by a retailer. The
price may be based on a Time-of-Use structure. Typically, the price is fixed on
an annual basis, but the price may also be dynamic. An example is shown in
Fig. 9.
- Wholesale forecast price based on an electricity forecast price of wholesale
market energy for the interval. Wholesale prices are established on a real-time
basis. An example is illustrated in Fig. 10. Fig. 10 depicts the network
forecast price 1002 and the wholesale forecast price 1004. A line has been
drawn to differentiate between the network forecast price 1002 and the
wholesale price 1004.
- Retail forecast price based on a smart meter tariff set by network operator.
The price may be based on a Time-of-Use structure. Typically, the price is
fixed on an annual basis, but the price may also be dynamic.
An example of fixed retail pricing may be for time-of-use consumer charges,
such as:
Peak: $0.36/kWh (Monday – Friday 2pm-8pm)
995717NZ_9414795_13
Shoulder: $0.13/kWh (7am-2pm, 8pm-10pm Monday-Friday, and
7am-10pm Saturday-Sunday.)
Off-Peak: $0.08/kWh (10pm-7am every day)
A related pricing approach may also apply at the network level.
Dynamic pricing may be, for example in a retail situation, twelve (12)
instances per annum of a rate of $2.50/kWh for any 2 hour period, with notification of
that period being advised no less than 30 minutes before the commencement of the
dynamic price period.
Upon completion of step 701, method 700 advances to step 702.
At step 702, forecast costs for one full day of intervals are determined. The
equation used to determine the forecast cost for an interval is:
FCi = (Reliability forecast price + Network forecast price + Wholesale
forecast price + Retail forecast price ) x Interval x kWh (eqn. 2)
i i i
FCi = forecast cost for interval i;
Interval = length of interval i in hour unit: and
kWh = Forecasted load at interval i (discussed hereinbefore).
Typically, two FCi values for two events, relating to a normal operation and a
power outage, are determined. The first FCi for a normal operation (hereinafter referred
to only as FCi) does not include the reliability forecast price , whilst the second FCi for
a power outage event (hereinafter referred to as FCi outage) includes the reliability
forecast price . Typically, a schedule for a normal operation and a schedule for a power
outage are determined using the FCi normal and the FCi outage, respectively.
Alternatively, the FCi outage and the corresponding schedule for a power outage event
may be determined when a power outage actually occurs.
For example, the load forecast (kWh ) between 9am and 10am, as shown in Fig.
11 with reference numeral 1102, is 0.75kWh. The forecast prices for the corresponding
interval are $50/kWh (802), $0.08/kWh (902), and $0.11/kWh (1004). The combined
forecast prices for the interval is $50.19/kWh. Thus, by using eqn. 2, the forecast cost
(FCi outage) for the interval between 9am and 10am is $37.6425, which is obtained by
multiplying $50.19 (the aggregate of forecast prices) with 1 hour (the interval of 9am to
995717NZ_9414795_13
10am) and with 0.75kWh (kWh ). On the other hand, the combined forecast prices for
FCi normal is $0.19/kWh and the FCi normal is $0.1425.
Fig. 12A illustrates an example of the forecasted cost (FCi) for one full day of
intervals based on the load forecast, shown in Fig. 11, and the aggregates of forecast
prices. Each interval is a 30 minute period.
Method 700 advances to step 703 when the forecast costs of intervals in a day
are calculated.
Step 703 sorts the forecasted costs (FCi) from highest to lowest. Fig. 12B
shows an example of the result of the sorting of step 703. For equally high cost periods,
a period later in the day takes priority over a period earlier in the day. Therefore, the
later period is listed first when the forecasted costs are sorted. Method 700 advances to
step 704.
Step 704 determines the most profitable intervals when the forecast cost is
greater than the battery discharge cost. The discharge cost is the cost of discharging the
energy storage device 106 of PA 100.
Fig. 12C shows an example of a discharge cost curve 1201 of a typical lead-
acid battery that may be use for, or as part of, the energy storage 106. The discharge
cost is based on tests carried out on an energy storage device by the energy storage
device manufacturer and after proprietary services. The tests determine the impact of
various depths of discharge, rates of charge and discharge, temperature of a battery on
the battery energy capacity, the losses from battery storage and battery lifetime.
For example, the discharge cost for a one hour interval of discharge at 75%
depth of discharge is approximately $0.16/kWh multiplied by one hour which equates to
$0.16. In another example, for a two hour interval of discharge at 100% depth of
discharge is approximately $0.175/kWh multiplied by 2 hours which equates to $0.35.
These examples do not take into account the reduction of available energy and capacity
(kW) as the battery is discharging. Thus, when determining the discharge schedule, the
method 700 minimises the load supply cost by ensuring that the battery 106 is not
discharged uneconomically.
An example of selecting the most profitable intervals is now demonstrated.
The sorted forecast cost (FCi) is compared with the battery discharge cost by comparing
the parameters, as diagrammatically shown in Fig. 12D. Fig. 12D is the merging of
Figs. 12B and 12C. Note that only the top ten intervals in regard of the forecast cost
995717NZ_9414795_13
(FCi) have been shown as the battery 106 is at 100% depth of discharge if the PA 100
enables discharging of the battery 106 for all ten intervals.
Fig. 12D represents battery discharge cost 1201and forecast cost 1202. The
left side of Fig. 12D automatically presents the profitable intervals, whereby the
forecast cost 1202 is above the discharge cost 1201. Typically, the intersection
between the forecast cost 1202 and the discharge cost 1201 signifies the end of the
profitable intervals. Thus, Fig. 12D shows that the PA 100 enables discharging of the
battery 106 only for the first four intervals, which correspond to the intervals of 16:00,
16:30, 17:00, and 17:30 of Fig. 12B.
The net effect of the above is that the determination of operating schedule of
the PA 100 includes consideration of the discharge cost of the energy storage device
106, consumer cost, retail price, network price, electricity market price, and electricity
supply cost. That consideration can therefore contribute to optimising the economic
lifetime of the battery 106, for example by avoiding (i) uneconomical excessive
discharge, (ii) uneconomical rates of discharge, and (iii) uneconomical heating or
cooling
Method 700 advances to step 706 upon completion of step 704.
At step 706, a discharge schedule is developed based on the selected intervals
of step 704. Fig. 12E shows the discharge schedule of the corresponding day of Fig.
12D. Fig. 13 shows another example of a discharge schedule of forecast intervals
maximising profit illustrating forecast discharge intervals 1302, maximum depths of
discharge of energy storage device 106, and forecast profit 1304 based upon the
discharge schedule. The depth of discharge depicted in Figs. 12E and 13 is the
maximum depth of discharge allowed for the intervals which has been determined to
maximise profit. Method 700 concludes upon completion of discharge schedule.
Fig. 14 is a flow diagram of a method for developing a charge schedule for PA
100. Method 1400 starts at step 1402 by removing intervals that has been assigned by
method 700 to be discharge intervals. Method 1400 advances to step 1404.
At step 1404, the optimisation application program 408 removes intervals
when the sum of charging load and forecast load would exceed the load capacity of the
mains supply 130. For example, the mains supply 130 may be limited to 240VAC 15A
for a GPO in Australia. If the forecast load for the interval is 10A and the bulk charging
load is 10A, then the sum of the forecast load and the bulk charging load is 20A, which
995717NZ_9414795_13
exceeds the capacity of mains supply 130 of 15A. The interval is consequently
removed from the charging schedule. Charging load levels is discussed below. Method
1400 progresses to step 1406.
At step 1406, a charge schedule for one day is developed based on forecast cost
(FCi), and battery recharge profiles and corresponding discharge costs.
Fig. 15 is a diagram showing an example of a lead-acid battery charging
process. The charging process of a lead-acid battery involves three stages: bulk
charging, absorption and float. At bulk charging, a current from mains supply 130 is
applied to the battery. Typically a charger forming part of the supply converter 104
controls the amount of voltage and current applied to the battery 106. At bulk charge
stage, the charger holds the charge current steady. Different charge current results in
different charging rate, which affects the battery energy capacity, battery life, and
battery discharge cost. Typically, the charger delivers most of the charge current at
maximum rate.
When a battery 106 reaches maximum allowable voltage, the battery 106 has
reached the absorption stage and the charger changes to holding the charge voltage at a
constant level. The constant charge voltage allows the battery 106 to “absorb” the
current. Consequently, the charging current declines. Typically, the absorption step
continues until current through the battery declines to about 2% of battery capacity
whereupon a float or trickle charge condition is maintained at the nominal battery
voltage. For example, a 100Ah battery would have 2Amps of absorption current
flowing through the battery.
At the float step, a lower charge current is applied to the battery for
maintaining a full charge state.
Forecast costs (FCi) are used for determining relatively low cost intervals.
Depending upon the charge current, bulk charging of the energy storage device 106 may
take only one interval or several intervals, and will affect the charge schedule.
A recharge profile is determined by the battery manufacturer and/or proprietary
battery testing by a third party based on actual testing carried out determining the
impact of various rates of charge on battery energy capacity, battery losses and battery
lifetime cost. A recharge profile also has a corresponding charge cost. For example,
when a battery 106 is bulk charged at an excessively high current, the battery 106
995717NZ_9414795_13
charges faster but consequently incurs more damage to the battery 106, which results in
a higher charge cost and shortening of the lifetime of battery 106.
For example, forecast costs for 30 minute intervals between a period of 8am to
10am are $0.25, $0.15, $0.20, and $0.30. A first recharge profile with low charge cost
may require two 30 minute intervals but a second recharge profile with medium charge
cost may require three 30 minute intervals. The optimisation application program 408
analyses the first and second recharge profiles using different combination of intervals
to determine a set of charge intervals with the lowest cost. Thus, the optimisation
application program 408 effectively optimises the charging current of the battery 106 to
determine the minimal battery charging costs.
Fig. 16 is an example of a charge schedule and average cost of charging the
energy storage device 106. As shown in Fig. 16, the intervals 1602 between midnight
and 7am are used to charge the battery 106, and there are different rates of charge as the
battery 106 goes through different charging stages. The associated energy cost 1604 for
charging the battery 106 is also shown.
Upon determining the optimal charge schedule, the optimisation application
program 408 updates the discharge cost to be used by method 700.
Fig. 17 depicts an example of a schedule for charge intervals 1702 and
discharge intervals 1704 with the associated network price 1706 shown. The figure
depicts an example whereby the charge intervals 1702 were performed when the
network price is relatively low and discharge intervals 1704 were performed when the
network price is relatively high.
Method 1400 concludes upon determining a charge schedule for PA 100.
Fig. 18 shows an interrupt method 1800 in determining an optimal schedule of
the PA 100. In this alternative method, the PA 100 discharges the battery at the
scheduled discharge periods and, at the same time, continuously monitors the electricity
price for a spike in the price. When the electricity price increases above a price
threshold, the operating schedule of the PA 100 is interrupted according to the method
1800 to discharge the battery.
The interrupt method 1800 is run by the Optimization Application Program 408
and is triggered when the electricity spot price exceeds a threshold. The threshold may
be determined by a user. The interrupt method 1800 commences at step 1802 to
discharge the battery 106. The method 1800 then proceeds to step 1803.
995717NZ_9414795_13
At step 1803, the Optimization Application Program 408 determines whether
the battery 106 has reached its minimum power level. As mentioned hereinbefore, the
minimum power level is set so that the battery 106 is not uneconomically depleted due
to an over-discharge. Although in some circumstances, it may be beneficial to set the
target level so as to completely exhaust the battery 106. For example, if the nominal
value of the battery is $20 and the complete discharge of the battery 106 prevents the
user from paying an electricity spot price spike of $30, then the Optimization
Application Program 408 sets the target level to 0 and allows the battery 106 to be
exhausted. If the battery 106 is at or below the target level (YES), the method 1800
concludes. Otherwise (NO), the method 1800 proceeds to step 1804.
Step 1804 determines if the electricity spot price still exceeds the threshold. If
the electricity spot price still exceeds the threshold (YES), the method 1800 returns to
step 1802 to continue discharge of the battery 106. Otherwise (NO), the method 1800
proceeds to step 1805. The check at step 1804 may be performed at an interval of 5
minutes, 10 minutes, or any other intervals deemed to be acceptable by the user.
At step 1805, the schedule of the PA 100 is redetermined according to the
method described hereinbefore. The method 1800 then concludes.
In one example of the operation of the interrupt method, a 2kWh battery is
used, the battery minimum power level is set by a user to be 1kWh, and the price
threshold for the electricity spot price is set by the user to be $5,000/MWh. Scheduled
discharge periods are at 10am to 11am, 3pm to 5pm, and 8pm to 10pm.
The PA 100 is enabled from 10am to 11am at a first scheduled discharge
period to discharge energy from the battery 106 to the load 132. At 12pm, the
electricity spot price exceeds the threshold (i.e., $5,000/MWh) and the PA 100 again
operates to discharge energy from the battery 106. The electricity spot price falls below
the threshold at 2pm and the discharge of the battery 106 stops. The battery 106 is now
at, say, 1.5kWh. Otherwise, the battery 106 continues discharging until it is discharged
to the predetermined level of 1.0kWh.
When battery discharge concludes, the Optimization Application Program 408
recomputes the discharge schedules and determines that the new discharge schedule is
now 4pm to 5pm and 8 pm to 9pm. The PA 100 then discharges at the new discharge
schedules.
995717NZ_9414795_13
In operation, the PA 100 provides for the periodic storage of electrical energy
at relatively low cost, and for consumption of that energy when mains supply costs are
relatively high. Notably the preferred implementation takes account of costs associated
with storing and supplying stored energy (e.g. battery replacement costs). The overall
effect of this is a reduction in energy supply related costs to energy retailers and/or
energy consumers, network operators and/or market operators.
For the energy retailer, the PA 100 provides a mechanism by which the impact
of high spot prices can be reduced, whilst increasing consumption when costs are lower,
thereby improving profit margins for the supplier.
There are three implementations of utilising the PA 100. The first
implementation is when an energy consumer buys the PA 100. In this case, the optimal
schedules of the PA 100 are based on minimising the electricity cost to the energy
consumer. Typically, battery 106 is discharged when prices to the consumers are
relatively high and is charged when prices to the consumers are relatively low.
The second implementation is when an energy retailer provides the PA 100 to
the energy consumer. As the provider of the PA 100, the energy retailer is only
concerned with minimising a retail supply cost of providing electrical energy to the
load. Thus, the energy retailer prefers energy to be consumed from the mains supply
only during periods of low electricity market and network pricing. Typically, PA 100
fulfils this goal by discharging the battery 106 when a combination of network and
wholesale electricity price is high and by charging the battery 106 when the same
combination of prices is low.
The third implementation is when a third party service provider leases the PA
100 to the energy consumers or retailers. The third party service provider typically has
agreements with energy retailers and network operators for effectively reducing
electricity consumption during peak periods. The third party service provider typically
has agreements with energy consumers for providing reliable energy supply, which may
be through determining a reliability price for various periods of the day. In this case,
the optimal schedules of the PA 100 are based upon maximising profit to the third party
service provider.
The arrangements described above provide for an optimal usage of a battery so
that a user may gain the full value of the battery. The battery provides value by
discharging to provide power at periods of high electricity prices and charging at
995717NZ_9414795_13
periods of low electricity prices. Therefore, a reduction of running costs of an electrical
load is the difference between the electricity prices during the discharging and charging
periods minus a depreciation value of the battery.
The depreciation value is the depreciation of the nominal value of the battery.
For example, a new battery may have a nominal value of $200 and a typical
depreciation value of $1/day through its normal usage pattern. Therefore, after 100
days, the nominal value of the battery is $100.
In some circumstances, the arrangements described above can allow a battery
to be completely exhausted and effectively destroy the battery if the value of exhausting
the battery outweighs the value of keeping the battery alive. For example, if a long-
used battery has a nominal value of $5 and the electricity spot price spike costs $15,
then the present arrangements described can allow the battery to be exhausted (i.e., fully
discharged), effectively killing the battery, to take advantage of the cost saving.
Industrial Applicability
The arrangements described are applicable to the electricity industries and
particularly for the electricity retailers.
The foregoing describes only some embodiments of the present invention, and
modifications and/or changes can be made thereto without departing from the scope and
spirit of the invention, the embodiments being illustrative and not restrictive.
In the context of this specification, the word “comprising” means “including
principally but not necessarily solely” or “having” or “including”, and not “consisting
only of”. Variations of the word "comprising", such as “comprise” and “comprises”
have correspondingly varied meanings.
Claims (36)
1. A power apparatus comprising: an input connectable to a mains electrical supply; 5 an energy storage device; a supply converter selectively connectable to an electrical supply to convert electrical power from the electrical supply to energy for storage in the energy storage device; a load converter arranged to convert energy from the energy storage device to 10 electrical power for supply to an electrical load; an output, selectively connectable to either of the input or the load converter, by which the electrical load is coupled to the apparatus to receive electrical power; and a control device, coupled to a communications network, configured to: receive, from the communications network, time-dependent electrical 15 pricing data associated with the mains electrical supply; determine a schedule, using at least the received time-dependent electrical pricing data, for each of (i) charging the energy storage device, (ii) supplying electrical power from the input to the output, and (iii) discharging the energy storage device to the output, wherein the determination of the 20 schedule for (iii) includes consideration of a discharge cost of the energy storage device; selectively connect the supply converter to the input according to the schedule; and selectively connect the output to either of the input or the load converter 25 according to the schedule to provide electrical power to the electrical load.
2. The power apparatus according to claim 1, wherein the device determining the schedule is further configured to: 30 receive a minimum power level for the energy storage device; and prevent discharging of the energy storage device below the minimum power level.
3. The power apparatus according to claim 1 or 2, wherein the device determining the schedule is further configured to: determine a load forecast based on historical electrical consumption data of the electrical load or a standard profile of the type of electrical load; and 5 determine the schedule for (iii) based on the determined load forecast.
4. The power apparatus according to any one of the preceding claims, wherein the device determining the schedule is further configured to: determine a forecast of the time-dependent electrical pricing data; and 10 determine the schedule based on the determined forecast of the time-dependent electrical pricing data.
5. The power apparatus according to any one of the preceding claims, wherein the device determining the schedule is further configured to: 15 receive a price threshold; and discharge the energy storage device if the time-dependent electrical pricing data exceeds the price threshold.
6. The power apparatus according to claim 5, wherein the device determining the 20 schedule is further configured to: redetermine the schedule for (i), (ii), or (iii) after the discharge of the energy storage device when the time-dependent electrical pricing data exceeds the price threshold. 25
7. The power apparatus according to any one of the preceding claims, wherein the control device is further configured to: receive, from the communications network, weather data; and determine the schedule using the received weather data. 30
8. The power apparatus according to any one of the preceding claims, wherein the determination of the schedule for (i) includes consideration of a charge cost of the energy storage device.
9. The power apparatus according to any one of the preceding claims, wherein the determination of the schedule for (i) includes consideration of a recharge profile of the energy storage device. 5
10. The power apparatus according to any one of the preceding claims, wherein the schedule for (iii) is determined based upon minimising a retail supply cost of providing electrical energy to the mains supply.
11. The power apparatus according to any one of the preceding claims, wherein the 10 schedule for (i) and/or (iii) are determined based upon maximising profit to a third party service provider.
12. The power apparatus according to any one of the preceding claims, wherein the schedule for (i) and/or (iii) are determined to optimise an economic lifetime of the 15 energy storage device.
13. The power apparatus according to any one of claims 1 to 9, wherein the schedule for (iii) is determined based upon minimising a wholesale supply cost to an energy retailer who provides the mains electrical supply to the power apparatus at a 20 retail supply cost.
14. The power apparatus according to any one of the preceding claims, wherein the energy storage device comprises a chemical battery; the supply converter comprises a rectifier and a battery charger; and the load converter comprises an inverter.
15. The power apparatus according to any one of the preceding claims, wherein the power apparatus further comprising: sensors for monitoring parameters of the energy storage device, wherein the sensors are coupled to the control device and the control device determines the schedule 30 using the monitored parameters, wherein the sensors comprise a temperature sensor for monitoring temperature of the energy storage device, and wherein the determination of the schedule includes consideration of the monitored temperature.
16. The power apparatus according to any one of the preceding claims, wherein the power apparatus is transportable.
17. A system comprising at least one power apparatus, a communications network, 5 and a server computer device, said power apparatus comprising: an input connectable to a mains electrical supply; an energy storage device; a supply converter selectively connectable to an electrical supply to 10 convert electrical power from the electrical supply to energy for storage in the energy storage device; a load converter arranged to convert energy from the energy storage device to electrical power for supply to an electrical load; an output, selectively connectable to either of the input or the load 15 converter, by which the electrical load is coupled to the apparatus to receive electrical power; and a control device, coupled to the communications network, configured to receive a schedule from the server computer device by which the control device selectively connects the supply converter to the input and selectively 20 connects the output to either of the input or the load converter according to the received schedule; and the server computer device is coupled to the communications network and is configured to: receive, from the communications network, time-dependent electrical 25 pricing data associated with the mains electrical supply; determine the schedule for the power apparatus for each of (i) charging the energy storage device, (ii) supplying power from the input to the output, and (iii) discharging the energy storage device to the output, wherein the determination of the schedule for (iii) includes consideration 30 of a discharge cost of the energy storage device, send the determined schedule to the control device.
18. The system according to claim 17, wherein the device determining the schedule is further configured to: receive a minimum power level for the energy storage device; and prevent discharging of the energy storage device below the minimum power 5 level.
19. The system according to claim 17 or 18, wherein the device determining the schedule is further configured to: determine a load forecast based on historical electrical consumption data of the 10 electrical load or a standard profile of the type of electrical load; and determine the schedule for (iii) based on the determined load forecast.
20. The system according to any one of claims 17 to 19, wherein the device determining the schedule is further configured to: 15 determine a forecast of the time-dependent electrical pricing data; and determine the schedule based on the determined forecast of the time-dependent electrical pricing data.
21. The system according to any one of claims 17 to 20, wherein the device 20 determining the schedule is further configured to: receive a price threshold; and discharge the energy storage device if the time-dependent electrical pricing data exceeds the price threshold. 25
22. The system according to claim 21, wherein the device determining the schedule is further configured to: redetermine the schedule for (i), (ii), or (iii) after the discharge of the energy storage device when the time-dependent electrical pricing data exceeds the price threshold.
23. The system according to any one of claims 17 to 22, wherein the control device is further configured to: receive, from the communications network, weather data; and determine the schedule using the received weather data.
24. The system according to any one of claims 17 to 23, wherein the determination of the schedule for (i) includes consideration of a charge cost of the energy storage 5 device.
25. The system according to any one of claims 17 to 24, wherein the determination of the schedule for (i) includes consideration of a recharge profile of the energy storage device.
26. The system according to any one of claims 17 to 25, wherein the schedule for (iii) is determined based upon minimising a retail supply cost of providing electrical energy to the mains supply. 15
27. The system according to any one of claims 17 to 26, wherein the schedule for (i) and/or (iii) are determined based upon maximising profit to a third party service provider.
28. The system according to any one of claims 17 to 27, wherein the schedule for 20 (i) and/or (iii) are determined to optimise an economic lifetime of the energy storage device.
29. The system according to any one of claims 17 to 25, wherein the schedule for (iii) is determined based upon minimising a wholesale supply cost to an energy retailer 25 who provides the mains electrical supply to the power apparatus at a retail supply cost.
30. The system according to any one of claims 17 to 29, wherein the energy storage device comprises a chemical battery; the supply converter comprises a rectifier and a battery charger; and the load converter comprises an inverter.
31. The system according to any one of claims 17 to 30, wherein the power apparatus further comprising: sensors for monitoring parameters of the energy storage device, wherein the sensors are coupled to the control device and the control device determines the schedule using the monitored parameters, wherein the sensors comprise a temperature sensor for monitoring temperature of the energy storage device, and wherein the determination of the schedule includes consideration of the monitored temperature.
32. The system according to any one of claims 17 to 31, wherein the power apparatus is transportable. 10
33. An application program, executable by a computerized processor for determining a schedule for an operation of a power apparatus, the power apparatus being configured to provide electrical power to an electrical load, the power apparatus comprising: an input connectable to a mains electrical supply; 15 an energy storage device; a supply converter selectively connectable to an electrical supply to convert electrical power from the electrical supply to energy for storage in the energy storage device; a load converter arranged to convert energy from the energy storage device to 20 electrical power for supply to an electrical load; an output, selectively connectable to either of the input or the load converter, by which the electrical load is coupled to the apparatus to receive electrical power; and a control apparatus configured for: selectively connecting the supply converter to the input according to 25 the schedule, and selectively connecting the output to either of the input or the load converter according to the schedule to provide electrical power to the electrical load; the application program comprising: 30 code for receiving, from a communications network, time-dependent electrical pricing data associated with the mains electrical supply; code for determining a load forecast based on historical electrical consumption data of the electrical load or a standard profile of the type of electrical load; code for determining a schedule for discharging the energy storage device to the electrical load based on the determined load forecast, discharge cost of the energy storage device, and the received time-dependent electrical pricing data; and code for determining a schedule for charging the energy storage device based 5 on the discharge schedule, a recharge profile of the energy storage device and the received time-dependent electrical pricing data.
34. The application program according to claim 33, wherein the code for determining a load forecast further considers a factor selected from the group of factors 10 consisting of: weather data; type of day; type of month; type of week; 15 type of season type of interval; and any combination of the above factors.
35. The application program according to claim 33, wherein the application 20 program is stored in a memory of the control apparatus which includes the computerized processor.
36. The application program according to claim 33, wherein the application program is stored and executable in a server computer and further comprises code for 25 transmitting the operating schedule from the server computer to the power apparatus via a communications network. Empower Energy Pty Ltd By the Attorneys for the Applicant 30 SPRUSON & FERGUSON Per:
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2011902973A AU2011902973A0 (en) | 2011-07-26 | Power apparatus | |
AU2011902973 | 2011-07-26 | ||
PCT/AU2012/000882 WO2013013267A1 (en) | 2011-07-26 | 2012-07-25 | Power apparatus |
Publications (2)
Publication Number | Publication Date |
---|---|
NZ621385A NZ621385A (en) | 2014-12-24 |
NZ621385B2 true NZ621385B2 (en) | 2015-03-25 |
Family
ID=
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