Field of the invention
[0001] The present invention relates generally to the field of
digital platforms of utility industries. More particularly, the
present invention relates to a system and a method for developing
5 unified digital platform based virtual power banks for power
management and settlement.
Background of the invention
[0002] Clean energy being the need of the current times, utility
industries (utilities)are developing new products and technology
10 for achieving Environment, Social and Governance (ESG)
objectives. One of the ESG objectives is meeting carbon emission
targets based on decarbonization operations. In order to meet
carbon emission targets, the contribution of utility industries
is essential for reducing the carbon footprint linked to carbon
15 emissions. One of the measures used for reducing carbon footprint
is energy transition entailing electricity generation from one
or more renewable energy sources (e.g., solar energy, wind energy,
biogas, geothermal energy, hydro energy, etc.).
20 [0003] It has been observed that no proper mechanism or interface
exists that allows end-consumers to access the renewable energy
sources for consumption. Further, existing digital platforms do
not have mechanisms in place to track sources of energy (e.g.,
as renewable, or conventional). Also, it has been observed that
25 energy consumption patterns associated with renewable energy
sources (e.g., batteries used in an Electric Vehicles (EV)) are
unpredictable, which may lead to one or more issues such as, but
are not limited to, inaccurate load management, grid failures,
power loss, voltage instability, inadequate power pricing
30 mechanism, and harmonic distortion. Further, existing digital
platforms do not provide functionalities to visualize power
sources which may be suitably utilized and re-utilized.
[0004] In light of the aforementioned drawbacks, there is a need
for a digital platform for access to power generated from
renewable energy sources and efficient power management and
5 settlement. There is a need for a system and a method for
developing unified digital platform based virtual power banks for
power management and settlement. Also, there is a need for a
system and a method which provides for enhanced transparency in
utilization of renewable energy sources by end-consumers for
10 accelerating renewable energy usage. Further, there is a need for
a system and a method which provides for tracking sources of
power generation. Furthermore, there is a need for a system and
a method which provides for locating and buying unused and used
power generated from renewable energy sources. Yet further, there
15 is a need for a system and a method which provides for assessing
one or more variables associated with real-time power supply and
demand.
Summary of the invention
20
[0005] In various embodiments of the invention, a system for
developing unified digital platform based virtual power banks is
provided. The system comprises a memory storing program
instructions, a processor executing program instructions stored
25 in the memory. The system is configured to derive a second data
type by analyzing one or more record types. The record types are
obtained from a first data type received from multiple sources
and stored in a database. The system is configured to generate
virtual power banks by employing the first data type and the
30 second data type fetched from the database. The virtual power
banks are associated with one or more attributes relating to
power management and settlement. Further, the system is
configured to generate one or more dynamic actionable items
relating to the virtual power banks from the first data type and the second data type. Further, the system is configured to
identify one or more variables that correspond to different types
of the dynamic actionable items for categorizing the dynamic
actionable items based on the identified variables. Lastly, the
5 system is configured to perform optimization operations on values
of each of the identified variables to obtain an optimized final
weightage value of the virtual power banks, accessed via a unified
digital platform, based on which one or more operational
parameters associated with the virtual power banks are
10 determined.
[0006] In various embodiments of the invention, a method for
developing unified digital platform based virtual power banks is
provided. The method is implemented by a processor configured to
15 execute instructions stored in a memory. The method comprises
deriving a second data type by analyzing one or more record types.
The record types are obtained from a first data type received
from multiple sources and stored in a database. The method
comprises generating virtual power banks by employing the first
20 data type and the second data type fetched from the database. The
virtual power banks are associated with one or more attributes
relating to power bank management and settlement. The method
comprises generating one or more dynamic actionable items
relating to the virtual power banks from the first data type and
25 the second data type. Further, the method comprises identifying
one or more variables that correspond to different types of the
dynamic actionable items for categorizing the dynamic actionable
items based on the identified variables. Lastly, the method
comprises performing optimization operations on values of each
30 of the identified variables associated with the dynamic
actionable items to obtain an optimized final weightage value of
the virtual power banks, accessed via the unified digital platform
(106a), based on which one or more operational parameters
associated with the virtual power banks are determined.
Brief description of the accompanying drawings
[0007] The present invention is described by way of embodiments
illustrated in the accompanying drawings wherein:
[0008] FIG. 1 is a detailed block diagram of a system for
5 developing unified digital platform based virtual power banks for
power management and settlement, in accordance with an embodiment
of the present invention;
[0009] FIG. 2 illustrates an exemplary flow diagram for providing
virtual power banks hosted on a unified cloud-based platform, in
10 accordance with an embodiment of the present invention;
[0010] FIG. 3 and FIG. 3A is a flowchart illustrating a method
for developing unified digital platform based virtual power banks
for power management and settlement, in accordance with an
embodiment of the present invention; and
15 [0011] FIG. 4 illustrates an exemplary computer system in which
various embodiments of the present invention may be implemented.
Detailed description of the invention
[0012] The present invention discloses a system and a method for
developing unified digital platform based virtual power banks for
20 power management and settlement. The present invention discloses
a system and a method for efficient utilization and re-utilization
of power generated from renewable energy sources. The present
invention discloses a system and a method which provides for
enhanced transparency in the utilization of power generated from
25 renewable energy sources by end-consumers for accelerating
renewable energy usage. Also, the present invention discloses a
system and a method which provides for tracking the source of
power. Further, the present invention discloses a system and a
method which provides for locating unused and used power generated
30 from renewable energy sources. Furthermore, the present invention discloses a system and a method which provides for segregating
the power generated from the renewable energy sources into small
quantum based on demand on the digital platform. Yet further, the
present invention discloses a system and a method which provides
5 for improved load forecasting demand associated with the
renewable energy source power to prevent grid failure, power
loss, voltage instability and harmonic distortion based on
automatically determining needs and requirements associated with
the consumer’s demand.
10
[0013] The disclosure is provided in order to enable a person
having ordinary skill in the art to practice the invention.
Exemplary embodiments herein are provided only for illustrative
purposes and various modifications will be readily apparent to
15 persons skilled in the art. The general principles defined herein
may be applied to other embodiments and applications without
departing from the scope of the invention. The terminology and
phraseology used herein is for the purpose of describing exemplary
embodiments and should not be considered limiting. Thus, the
20 present invention is to be accorded the widest scope encompassing
numerous alternatives, modifications, and equivalents consistent
with the principles and features disclosed herein. For purposes
of clarity, details relating to technical material that is known
in the technical fields related to the invention have been briefly
25 described or omitted so as not to unnecessarily obscure the
present invention.
[0014] The present invention would now be discussed in context
of embodiments as illustrated in the accompanying drawings.
[0015] FIG. 1 is a detailed block diagram of a system 100 for
30 developing unified digital platform based virtual power banks for
power management and settlement, in accordance with various
embodiments of the present invention. Referring to FIG. 1, in an
embodiment of the present invention, the system 100 is in communication with a data source unit 114 and a user device 116
via a communication channel (not shown). In an exemplary
embodiment of the present invention, the user device 116 includes
electronic devices such as a smartphone, a computer and a laptop.
5 The communication channel (not shown) may include, but is not
limited to, a physical transmission medium, such as, a wire, or
a logical connection over a multiplexed medium, such as, a radio
channel in telecommunications and computer networking. Examples
of radio channel in telecommunications and computer networking
10 may include, but are not limited to, a Local Area Network (LAN),
a Metropolitan Area Network (MAN) and a Wide Area Network (WAN).
[0016] In an embodiment of the present invention, the system 100
is configured with a built-in mechanism for generating unified
digital platform based virtual power banks. The system 100 is
15 configured to generate the virtual power banks on a unified cloudbased platform, as illustrated in Fig. 2, in accordance with an
embodiment of the present invention. In an exemplary embodiment
of the present invention, the unified cloud-based platform may
include, but is not limited to, Azure®, Amazon Web Services®
20 (AWS), etc., hosted on a private cloud. The system 100 is
configured to provide a bi-directional communication between an
end-consumer and a utility retailer through a unified digital
platform 106a. The system 100 provides for power/energy management
and settlement (e.g., purchase and sale of renewable energy) in
25 the form of the virtual power banks through the unified digital
platform 106a. For example, the purchased virtual power bank may
be used by end-consumers for charging Electric Vehicles (EVs) with
clean energy generated from renewable sources and also set off
units in monthly utility bills, within permissible time limit.
30 The remaining unutilized energy of the virtual power banks may
also be resold to other consumers through the unified digital
platform 106a by the end-consumers by creating their own power
banks. Through this unified digital platform 106a, any consumer
who self-generates electricity (e.g., through roof top solar panels) can also feed their self-generated renewable energy to
power grids or the remaining power stored in their EV batteries
to feed EV batteries of other consumers.
[0017] In an embodiment of the present invention, the system
5 comprises a database 102, a data analytics unit 104, a virtual
power bank generation unit 106 and a visualization unit 108. In
an embodiment of the present invention, the system 100 comprises
a processor 110 and a memory 112. In various embodiments of the
present invention, the system 100 has multiple units which work
10 in conjunction with each other for generating a smart contract
associated with power generated from renewable energy sources.
The various units of the system 100 are operated via the processor
110 specifically programmed to execute instructions stored in the
memory 112 for executing respective functionalities of the units
15 of the system 100 in accordance with various embodiments of the
present invention.
[0018] In an embodiment of the present invention, the system 100
is implemented in a cloud computing architecture in which data,
applications, services, and other sources are stored and delivered
20 through shared datacenters. In an exemplary embodiment of the
present invention, the functionalities of the system 100 are
delivered to a user as a Platform as a Service (PaaS) over a
communication network.
[0019] In operation, in an embodiment of the present invention,
25 the database 102 is configured to receive a first data type from
multiple sources. The first data type represents data relating to
power usage and requirements associated with multiple sources. The
multiple sources include, but are not limited to, renewable energy
generators, utility retailers, end-consumers, and Electric
30 Vehicles (EV) charging stations. The first data type is stored in
the database 102 in the form of different record types associated
with each of the multiple sources. The one or more record types may include, but are not limited to, Power Purchase Agreement
(PPA) records associated with the renewable energy generators,
Renewable Purchase Obligation (RPO) records associated with the
utility retailers, Power Bank (PB) contract records associated
5 with end-consumers, billing and payment records associated with
the EV charging stations. The first data type also includes data
related to real-time power supply and demand received from one or
more utilities and data related to multiple variables which are
stored in the database 102 (explained in detailed in later sections
10 of the specification). In an embodiment of the present invention,
the data analytics unit 104 fetches the record types from the
database 102 for analysis and processing. In an embodiment of the
present invention, the data analytics unit 104 is configured with
one or more functionalities based on which the record types are
15 analyzed and processed to derive a second data type. The one or
more functionalities include, but are not limited to, enterprise
resource planning, Power Purchase Agreement (PPA) and Renewable
Purchase Obligation (RPO) record management, Power Bank (PB)
contract management, contract and transaction management, and end20 consumer data management. The data analytics unit 104 generates
the second data type after analysis and processing of the record
types and stores the second data type in the database 102. In an
exemplary embodiment of the present invention, the second data
type includes data related to power usage. In an example, the
25 second data type includes a quantum of power to be supplied i.e.,
capacity of the power banks, for instance power banks may be of a
smaller capacity of 100 units or higher capacity of 1000 units.
In another example, the second data type includes duration of
supply per terms of usage of the power banks, for instance, some
30 power banks may hold a shorter duration of 3 months or 6 months,
while some other power banks may have a higher duration of 1 year,
2 years, etc. In yet another example, the second data type includes
negotiated price of the power bank, for instance, higher the
purchasing quantity of power banks by the end-consumer lesser could be the price, and in another instance, higher the duration lesser
could be the price. In another example, the second data type includes
penalties for non-compliance related to power supply and usage per
the RPO. In yet another example, the second data type includes
5 data related to end-consumer segments, for instance, category of
consumers such as residential consumers buying for self-usage and
commercial consumers buying for re-selling. In another example,
the second data type includes sources of power generation, for
instance, solar generators are available in markets at lower feed
10 in tariff rates.
[0020] In an embodiment of the present invention, the virtual
power bank generation unit 106 generates virtual power banks by
employing the first data type and the second data type fetched
from the database 102. The virtual power banks are small quantum
15 of power entities hosted in the cloud-based unified digital
platform 106a. The virtual power banks are associated with one or
more attributes including, but not limited to, different
quantities of power (in kWh), time range for power utilization and
price associated with the power based on the sources of renewable
20 energy received from the renewable energy generators.
[0021] In an embodiment of the present invention, the virtual
power banks utilize end-to-end encryption with blockchain
technology containing smart contracts for its use by endconsumers, utility retailers and renewable energy generators. The
25 smart contracts are digital logic agreements in a blockchain that
contain terms which are automatically executed when pre-defined
conditions related to power management and settlement through the
virtual power banks are met. The smart contracts are generated
based on the PPA record type of the second data type for the
30 virtual power banks. In an exemplary embodiment of the present
invention, the virtual power banks are operated based on four
types of smart contracts. A first type of smart contract relates
to PPA between the renewable energy generator and a utility retailer for trading renewable energy capacity. In an embodiment
of the present invention, the first type of smart contract has a
unique hash key function, which is generated between utility
retailors and renewable energy generators and provides one or more
5 predetermined conditions including, but are not limited to,
quantity, price, and timeline of generated power. The first type
of smart contract (referred to as a parent smart contract) is
bifurcated into multiple sub-contracts (which are associated with
virtual power banks) and are hosted in the cloud platform. Further,
10 all the sub-contracts have a respective hash function, which is
unique and random, thereby ensuring end-to-end encryption of the
first type of smart contract. The hash function may be backtracked
for generating the first type of contract. Further, the subcontracts are purchased and sold multiple times and each sub15 contract has a transaction appending buyer/seller token ID. A
second type of smart contract relates to a PPA between the utility
retailer and the end-consumer for trading the power bank. A third
type of smart contract relates to a PPA between the end-consumer
and EV retailer for transfer of energy for charging of the EV
20 vehicles. A fourth type of smart contract relates to a peer-topeer contract between consumers with other end-consumers or with
retailers in selling their unused power banks or the self-generated
renewable energy power or power stored in the EV batteries.
[0022] In an embodiment of the present invention, the virtual
25 power bank generation unit 106 generates dynamic actionable items
which relate to one or more operational parameters of the virtual
power banks. The dynamic actionable items are generated from the
first data type that includes data related to real-time power
supply and demand received from one or more utilities and data
30 related to multiple variables, and the second data type fetched
from the database 102. The data related to real-time power demand
includes demand from both ends i.e., availability of power banks
from utilities and availability of end-consumers to purchase the power banks. The multiple variables are dynamic in nature and are
identified based on empirical studies.
[0023] In one example, the variables include season or weather
i.e., availability of solar radiation is usually less in winter
5 and high in summers. In another example, the variables include
landscape conditions for instance, renewable energy sources and
their availability has a high impact on landscape types, i.e.,
solar intensity is high in low-lying areas like urban, semi-urban,
rural, semi-rural and remote areas, etc. In yet another example,
10 the variables include current market price i.e., the current
renewable energy unit price in the market. In another example, the
variables include frequent purchaser data. In yet another example,
the variables include forecasted market price i.e., forecasted
energy unit price of the market.
15 [0024] In an exemplary embodiment of the present invention, the
dynamic actionable items include, but are not limited to, a
segmented actionable item, a time-based actionable item, a peak
actionable item, a penetration actionable item, a competitive
actionable item, and a bulk actionable item. The segmented
20 actionable item relates to an operational parameter of the virtual
power bank based on geographical locations, frequency of usage,
and duration of usage. The time-based actionable item relates to
an operational parameter based on weather, and source of power
generation. The peak actionable item relates to an operational
25 parameter according to high demand times, and leverage data
associated with the other power providers or generators, such as
inventory or availability. The penetration actionable item relates
to an operational parameter based on settling a lower value of
power usage for effective management. The competitive actionable
30 item relates to an operational parameter based on setting of a
value of the power usage according to other power generators. The
bulk actionable item relates to an operational parameter based on
providing of a lower value to end-consumers for bulk usage.
[0025] In an embodiment of the present invention, the virtual
power bank generation unit 106 generates the dynamic actionable
items by employing the first data type and the second data type
based on a sequence of steps. Firstly, the virtual power bank
5 generation unit 106 determines a base value, which is a power
procurement value between the end-consumer and the renewable
energy generator based on the PPA between the utility and the
renewable energy generator. Secondly, the virtual power bank
generation unit 106 determines a utility value associated with
10 infrastructure usage allowance based on which a threshold value
is determined. The threshold value represents a minimum value below
which the virtual power bank cannot be operated. Finally, the
virtual power bank generation unit 106 determines an optimized
weightage value of the virtual power banks based on the first data
15 type and the second data type.
[0026] In an embodiment of the present invention, the virtual
power bank generation unit 106 determines the optimized weightage
value of the virtual power banks by initially identifying and
processing the one or more multiple variables and determining one
20 or more sub-variables associated with each of the multiple
variables. For example, if the variable is weather and power is
generated from solar energy, then the sub-variables may include,
but are not limited to, sunny weather, cloudy weather, windy
weather, rainy weather, snowy weather, and foggy weather, which
25 may affect the power generation from the solar energy. In an
embodiment of the present invention, the virtual power bank
generation unit 106 computes an initial weightage value for each
of the sub-variables. The virtual power bank generation unit 106
carries out a first optimization operation for optimizing the
30 computed initial weightage values for each of the sub-variables.
In an exemplary embodiment of the present invention, the first
optimization operation is carried out by employing machine
learning and deep learning techniques. During the first
optimization operation, a model is trained iteratively that results in a maximum and minimum function evaluation. The results
in every iteration are compared with each other by changing
hyperparameters in each step until optimum results are obtained.
Subsequently, an accurate model with less error rate is generated.
5 In an exemplary embodiment of the present invention, the generated
model is optimized by using two optimization techniques including,
but are not limited to, a gradient descent optimization technique
and a stochastic gradient descent optimization technique. Based
on the gradient descent optimization technique, variables’ weights
10 are updated iteratively in the opposite direction of one or more
gradients of an objective function, which causes the model to find
a target and converge an optimal value of the objective function
based on each update of the weights. The convergence to the optimal
value of the optimal function provides the optimal weights for the
15 features. Based on the stochastic gradient descent technique,
gradients per iteration are updated using one sample randomly
instead of directly computing the exact value of the gradient.
Therefore, stochastic gradient descent technique provides an
unbiased estimate of the real gradient. Advantageously, the
20 stochastic gradient descent technique optimization method reduces
the update time for processing a large number of samples and
removes computational redundancy. Further, a proper learning rate
of the model is determined for the stochastic gradient descent
technique. The learning rate provides flexibility to the model by
25 discarding certain segments of the data, however, the model may
discard certain segments of data when the learning rate is high.
Therefore, a low learning rate is carried out. The learning rate
may be of different types including, but is not limited to, an
adaptive gradient technique (Adagrad)that provides weights with a
30 high gradient having low learning rate and vice versa, a Root Mean
Squared Propagation (RMSprop) technique that adjusts the Adagrad
method such that it reduces its monotonically decreasing learning
rate, an Adam technique, which is similar to RMSProp but provides
momentum, and an Alternating Direction Method of Multipliers (ADMM) technique that provides the stochastic gradient descent
variants which is widely used in deep neural network techniques.
Further, the gradient descent and AdaGrad technique varies with
respect to each other based on the learning rate, which is not
5 fixed, and the learning rate is computed using all the historical
gradients accumulated up to the latest iteration. The optimized
initial weightage values computed for each of the sub-variables
have been illustrated in table 1.
[0027] In an embodiment of the present invention, the virtual
power bank generation unit 106 captures data associated with the
sub-variables for an end-consumer who accesses the unified digital
15 platform 106a for obtaining the virtual power bank. Based on the captured data, the virtual power bank generation unit 106 analyzes
interdependency between each sub-variable with respect to another
sub-variable in a matrix form across rows and columns of the
matrix, as illustrated in table 2. Based on the interdependency
5 analysis, the virtual power bank generation unit 106 adds the
initial weightage values of the interdepending sub-variables and
subsequently assigns a label (e.g., high, closer to high, medium,
greater than medium, less than medium, low, etc.) to each of the
sub-variables. The virtual power bank generation unit 106 then
10 replaces the labels associated with each sub-variable with a
numerical value. The virtual power bank generation unit 106 then
carries out a second optimization operation for optimizing the
initial weightage values by computing an average of the numerical
values assigned to each of the sub-variable in either of each row
15 or column of the matrix to generate a first weightage value for
each of the variables, as illustrated in Table 3.
[0028] In an embodiment of the present invention, the virtual
power bank generation unit 106 identifies the variables that
correspond to the different types of dynamic actionable items and
5 categorizes the dynamic actionable items based on the identified
variables, as illustrated in table 4. The virtual power bank
generation unit 106 then replaces the identified variables with
the computed final weightage values corresponding to each of the
variables. The virtual power bank generation unit 106 then carries
10 out a third optimization operation by computing an average of the
final weightage values for each of the variables associated with
the dynamic actionable items for each row of the matrix. Finally,
the virtual power bank generation unit 106 determines a maximum average value among all the computed average values associated
with all the dynamic actionable items across each column of the
matrix to determine a second weightage value, as illustrated in
table 5. The second weightage value is the optimized final
5 weightage value of the virtual power bank for which the endconsumer accessed the unified digital platform 106a. Likewise,
optimized final weightage values are computed for multiple virtual
power banks for end-consumers who access the unified digital
platform 106a for obtaining virtual power banks.
[0029] In an embodiment of the present invention, the virtual
power bank generation unit 106 determines the operational
5 parameters for the virtual powers bank by carrying out a
multiplication operation between the computed second weightage
value and the threshold value, and then adjusting against the base
value. The operational parameter is dynamic in nature, as it is
based on dynamic actionable items that depend on the variables,
10 which vary with respect to end-consumer requirements.
[0030] In an embodiment of the present invention, the
visualization unit 108 is configured to render a Graphical User
Interface (GUI) on the user device 116. The visualization unit 108
provides presentation and visualization of functionalities related
15 to virtual power banks including, but not limited to, a dashboard,
an application, and a portal for the end-consumers to access the
unified digital platform 106a for operating or generating the
virtual power banks. The application may be scalable depending on
different functionalities provided by the visualization unit 108
20 including, but not limited to, utility administration related
functions and end-consumer use cases. In an embodiment of the
present invention, the visualization unit 108 provides a
functionality for tracing the renewable energy source used to
generate the power obtainable as virtual power banks. Further, the
25 visualization unit 108 provides a functionality of determining the
unused power associated with the end-consumer for re-using by
listing the end-consumers having unused power. For example, if
power is generated by using a roof top solar panel and the power
is used to charge EV, then in order to feed excess power into the
30 grid through net metering, the end-consumer may separately list
the unused excess power to be fed to the grid on the cloud-based
platform via the virtual power bank.
[0031] Fig. 3 and Fig. 3A is a flowchart illustrating a method
for developing unified digital platform based virtual power banks
for power management and settlement, in accordance with various
embodiments of the present invention.
5
[0032] At step 302, the first data type is received from multiple
sources and stored in a database 102 in the form of record types.
In an embodiment of the present invention, the first data type
represents data relating to power usage and requirements
10 associated with multiple sources. The multiple sources include,
but are not limited to, renewable energy generators, utility
retailers, end-consumers, and Electric Vehicles (EV) charging
stations. The first data type is stored in the database 102 in the
form of different record types associated with each of the multiple
15 sources. The one or more record types may include, but are not
limited to, Power Purchase Agreement (PPA) records associated with
the renewable energy generators, Renewable Purchase Obligation
(RPO) records associated with the utility retailers, Power Bank
(PB) contract records associated with end-consumers, billing and
20 payment records associated with the EV charging stations. The first
data type also includes data related to real-time power supply and
demand received from one or more utilities and data related to
multiple variables which are stored in the database 102. In an
embodiment of the present invention, the record types are fetched
25 from the database 102 for analysis and processing. At step 304,
the record types are analyzed and processed for generating a second
data type. In an embodiment of the present invention, one or more
functionalities are utilized for analysis and processing of the
record types to derive the second data type. The one or more
30 functionalities include, but are not limited to, enterprise
resource planning, Power Purchase Agreement (PPA) and Renewable
Purchase Obligation (RPO) record management, Power Bank (PB)
contract management, contract and transaction management, and end consumer data management. The second data type is generated after analysis and processing of the record types and the second data
type is stored in the database 102. In an exemplary embodiment of
the present invention, the second data type includes data related
to power usage. In an example, the second data type includes a
5 quantum of power to be supplied i.e., capacity of the power banks,
for instance power banks may be of a smaller capacity of 100 units
or higher capacity of 1000 units. In another example, the second
data type includes duration of supply per terms of usage of the
power banks, for instance, some power banks may hold a shorter
10 duration of 3 months or 6 months, while some other power banks may
have a higher duration of 1 year, 2 years, etc. In yet another
example, the second data type includes negotiated price of the
power bank, for instance, higher the purchasing quantity of power
banks by the end-consumer lesser could be the price, and in another
15 instance, higher the duration lesser could be the price. In another
example, the second data type includes penalties for noncompliance related to power supply and usage per the RPO. In yet
another example, the second data type includes data related to
end-consumer segments, for instance, category of consumers such
20 as residential consumers buying for self-usage and commercial
consumers buying for re-selling. In another example, the second
data type includes sources of power generation, for instance, solar
generators are available in markets at lower feed in tariff rates.
25 [0033] At step 306, virtual power banks are generated by
employing the first data type and the second data type. In an
embodiment of the present invention, virtual power banks are
generated by employing the first data type and the second data
type fetched from the database 102. The virtual power banks are
30 small quantum of power entities hosted in the cloud-based unified
digital platform. The virtual power banks are associated with one
or more attributes including, but not limited to, different
quantities of power (in kWh), time range for power utilization and price associated with the power based on the sources of renewable
energy received from the renewable energy generators.
[0034] In an embodiment of the present invention, the virtual
5 power banks utilize end-to-end encryption with blockchain
technology containing smart contracts for its use by endconsumers, utility retailers and renewable energy generators. The
smart contracts are digital logic agreements in a blockchain that
contain terms which are automatically executed when pre-defined
10 conditions related to power management and settlement through the
virtual power banks are met. The smart contracts are generated
based on the PPA record type of the second data type for the
virtual power banks. In an exemplary embodiment of the present
invention, the virtual power banks are operated based on four
15 types of smart contracts. The first type of smart contract relates
to PPA between the renewable energy generator and a utility
retailer for trading renewable energy capacity. In an embodiment
of the present invention, the first type of smart contract has a
unique hash key function, which is generated between utility
20 retailors and renewable energy generators and provides one or more
predetermined conditions such as, but are not limited to, quantity,
price, and timeline of generated power. The first type of smart
contract (referred to as a parent smart contract) is bifurcated
into multiple sub-contracts (which are associated with the virtual
25 power banks) and are hosted in the cloud platform. Further, all
the sub-contracts have a respective hash function, which is unique
and random, thereby ensuring end-to-end encryption of the first
type of smart contract. The hash function may be backtracked for
generating the first type of contract. Further, the sub-contracts
30 are purchased and sold multiple times and each sub-contract has a
transaction appending buyer/seller token ID. A second type of smart
contract relates to a PPA between the utility retailer and the
end-consumer for trading with the power bank. A third type of
smart contract relates to a PPA between the end-consumer and EV retailer for transfer of energy for charging of the EV vehicles.
A fourth type of smart contract relates to a peer-to-peer contract
between consumers with other end-consumers or with retailers in
selling their unused power banks or the self-generated renewable
5 energy power or power stored in the EV batteries.
[0035] At step 308, dynamic actionable items are generated which
relate to an operational parameter of the virtual power banks. In
an embodiment of the present invention, the dynamic actionable
10 items are generated from the first data type that includes data
related to real-time power supply and demand received from one or
more utilities and data related to multiple variables, and the
second data type fetched from the database 102. The data related
to real-time power demand includes demand from both ends i.e.,
15 availability of power banks from utilities and availability of
end-consumers to purchase the power banks. The multiple variables
are dynamic in nature and are identified based on empirical
studies.
20 [0036] In one example, the variables include season or weather
i.e., availability of solar radiation is usually less in winter
and high in summers. In another example, the variables include
landscape conditions for instance, renewable energy sources and
their availability has a high impact on landscape types, i.e.,
25 solar intensity is high in low-lying areas like urban, semi-urban,
rural, semi-rural and remote areas, etc. In yet another example,
the variables include the current market price i.e., the current
renewable energy unit price in the market. In another example, the
variables include frequent purchaser data. In yet another example,
30 the variables include forecasted market price i.e., forecasted
energy unit price of the market.
[0037] In an exemplary embodiment of the present invention, the
dynamic actionable items include, but are not limited to, a
segmented actionable item, a time-based actionable item, a peak actionable item, a penetration actionable item, a competitive
actionable item, and a bulk actionable item. The segmented
actionable item relates to the operational parameter of the virtual
power bank based on geographical locations, frequency of usage,
5 and duration of usage. The time-based actionable item relates to
the operational parameter based on weather, and source of power
generation. The peak actionable item relates to the operational
parameter according to high demand times, and leverage data
associated with the other power providers or generators, such as
10 inventory or availability. The penetration actionable item relates
to the operational parameter based on settling a lower value of
power usage for effective management. The competitive actionable
item relates to the operational parameter based on setting of a
value of the power usage according to other power generators. The
15 bulk actionable item relates to the operational parameter based
on providing of a lower value to end-consumers for bulk usage.
[0038] In an embodiment of the present invention, the dynamic
actionable items are generated by employing the first data type
and the second data type based on a sequence of steps. Firstly, a
20 base value, which is a power procurement value between the endconsumer and the renewable energy generator, is determined based
on the PPA between the utility and the renewable energy generator.
Secondly, a utility value associated with infrastructure usage
allowance is determined based on which a threshold value is
25 determined. The threshold value represents a minimum value below
which the virtual power bank cannot be operated. Finally, an
optimized weightage value of the virtual power banks is determined
based on the first data type and the second data type.
30 [0039] At step 310, an optimized weightage value of the virtual
power banks is determined by initially identifying and processing
one or more multiple variables and determining one or more subvariables associated with each of the multiple variables. In an
embodiment of the present invention, for example, if the variable is weather and power is generated from solar energy, then the subvariables may include, but are not limited to, sunny weather,
cloudy weather, windy weather, rainy weather, snowy weather, and
foggy weather, which may affect the power generation from the
5 solar energy. In an embodiment of the present invention, the
virtual power bank generation unit 106 computes an initial
weightage value for each of the sub-variables. The virtual power
bank generation unit 106 carries out a first optimization operation
for optimizing the computed initial weightage values for each of
10 the sub-variables. In an exemplary embodiment of the present
invention, the first optimization operation is carried out by
employing machine learning and deep learning techniques. During
the first optimization operation, a model is trained iteratively
that results in a maximum and minimum function evaluation. The
15 results in every iteration are compared with each other by changing
hyperparameters in each step until optimum results are obtained.
Subsequently, an accurate model with less error rate is generated.
In an exemplary embodiment of the present invention, the generated
model is optimized by employing two optimization techniques
20 including, but are not limited to, a gradient descent optimization
technique and a stochastic gradient descent optimization
technique. Based on the gradient descent optimization technique,
variables weights are updated iteratively in the opposite
direction of one or more gradients of an objective function, which
25 causes the model to find a target and converge the optimal value
of the objective function based on each update of the weights. The
convergence to the optimal value of the optimal function provides
the optimal weights for the features. Based on the stochastic
gradient descent technique, the gradients per iteration are
30 updated using one sample randomly instead of directly computing
the exact value of the gradient. Therefore, stochastic gradient
descent technique provides an unbiased estimate of the real
gradient. Advantageously, the stochastic gradient descent
technique optimization method reduces update time for processing large numbers of samples and removes computational redundancy.
Further, a proper learning rate of the model is determined for the
stochastic gradient descent technique. The learning rate provides
flexibility to the model by discarding certain segments of the
5 data, however, the model may discard certain segments of data when
the learning rate is high. Therefore, a low learning rate is
carried out. The learning rate may be of different types including,
but is not limited to, an adaptive gradient technique (Adagrad)that
provides weights with a high gradient having low learning rate and
10 vice versa, a Root Mean Squared Propagation (RMSprop) technique
that adjusts the Adagrad method such that it reduces its
monotonically decreasing learning rate, an Adam technique, which
is similar to RMSProp but provides momentum, and an Alternating
Direction Method of Multipliers (ADMM) technique that provides the
15 stochastic gradient descent variants which is widely used in deep
neural network techniques. Further, the gradient descent and
AdaGrad technique varies with respect to each other based on the
learning rate, which is not fixed and the learning rate is computed
using all the historical gradients accumulated up to the latest
20 iteration. The optimized initial weightage values computed for
each of the sub-variables have been illustrated in table 1.
[0040] In an embodiment of the present invention, data associated
with the sub-variables is captured for an end-consumer who accesses
25 the unified digital platform for obtaining the virtual power bank.
Based on the captured data, interdependency between each subvariable with respect to another sub-variable is analyzed in a
matrix form across rows and columns of the matrix, as illustrated
in table 2. Based on the interdependency analysis, the initial
30 weightage values of the interdepending sub-variables are added and
subsequently a label is assigned (e.g., high, closer to high,
medium, greater than medium, less than medium, low, etc.) to each
of the sub-variables. The labels associated with each sub-variable
are replaced with a numerical value. A second optimization operation carried out for optimizing the initial weightage values
by computing an average of the numerical values assigned to each
of the sub-variable in either of each row or column of the matrix
to generate a first weightage value for each of the variables, as
5 illustrated in Table 3.
[0041] At step 312, the variables that correspond to different
types of dynamic actionable items are identified and the dynamic
actionable items are categorized based on the identified
10 variables, as illustrated in Table 4. In an embodiment of the
present invention, the identified variables are replaced with the
computed final weightage values corresponding to each of the
variables. A third optimization operation is carried out by
computing an average of the final weightage values for each of the
15 variables associated with the dynamic actionable items for each
row of the matrix. Finally, a maximum average value is determined
among all the computed average values associated with all the
dynamic actionable items across each column of the matrix to
determine a second weightage value, as illustrated in table 5. The
20 second weightage value is the optimized final weightage value of
the virtual power bank for which the end-consumer accessed the
unified digital platform. Likewise, optimized final weightage
values are computed for multiple virtual power banks for endconsumers who access the unified digital platform 106a for
25 obtaining virtual power banks.
[0042] At step 314, the operational parameter for the virtual
power bank is determined. In an embodiment of the present
invention, the operational parameter for the virtual power bank
30 is determined by carrying out a multiplication operation between
the computed second weightage value and the threshold value, and
then adjusting against the base value. The operational parameter
is dynamic in nature, as it is based on dynamic actionable items that depend on the variables, which vary with respect to endconsumer requirements.
[0043] At step 316, a Graphical User Interface (GUI) is rendered
5 for presentation and visualization of functionalities related to
virtual power banks. In an embodiment of the present invention,
presentation and visualization of functionalities related to
virtual power banks are provided including, but not limited to, a
dashboard, an application, and a portal for the end-consumers to
10 access the unified digital platform 106a for operating or
generating the virtual power banks. The application may be scalable
depending on different functionalities including, but not limited
to, utility administration related functions and end-consumer use
cases. In an embodiment of the present invention, a functionality
15 is provided for tracing the renewable energy source used to
generate the power obtainable as virtual power banks. Further, a
functionality of determining the unused power associated with the
end-consumer is provided for re-using by listing the end-consumers
having unused power. For example, if power is generated by using
20 a roof top solar panel and the power is used to charge EV, then
in order to feed excess power into the grid through net metering,
the end-consumer may separately list the unused excess power to
be fed to the grid on the cloud-based platform via the virtual
power bank.
25
[0044] Advantageously, in accordance with various embodiments of
the present invention, the present invention provides for access
to end-consumers to power generated from renewable energy sources
via a cloud-based unified digital platform. The present invention
30 provides for efficient power management and settlement via the
unified digital platform. Also, the present invention provides for
utilization and re-utilization of power generated from renewable
energy sources. Further, the present invention provides for
tracking and locating the type of source of power at a granular level. Yet further, the present invention provides for improved
load forecasting by the power generator associated with the
renewable energy source power ensuring grid stability.
Furthermore, the present invention provides for enhanced
5 transparency in the utilization of power generated using renewable
energy sources by end-consumers for accelerating renewable energy
usage. Yet further, the present invention provides for effective
charging of EVs.
10 [0045] FIG. 4 illustrates an exemplary computer system in which
various embodiments of the present invention may be implemented.
The computer system 402 comprises a processor 404 and a memory
406. The processor 404 executes program instructions and is a real
processor. The computer system 402 is not intended to suggest any
15 limitation as to scope of use or functionality of described
embodiments. For example, the computer system 402 may include, but
not limited to, a programmed microprocessor, a micro-controller,
a peripheral integrated circuit element, and other devices or
arrangements of devices that are capable of implementing the steps
20 that constitute the method of the present invention. In an
embodiment of the present invention, the memory 406 may store
software for implementing various embodiments of the present
invention. The computer system 402 may have additional components.
For example, the computer system 402 includes one or more
25 communication channels 408, one or more input devices 410, one or
more output devices 412, and storage 414. An interconnection
mechanism (not shown) such as a bus, controller, or network,
interconnects the components of the computer system 402. In various
embodiments of the present invention, operating system software
30 (not shown) provides an operating environment for various
softwares executing in the computer system 402 and manages
different functionalities of the components of the computer system
402.
[0046] The communication channel(s) 408 allow communication
over a communication medium to various other computing entities.
The communication medium provides information such as program
instructions, or other data in a communication media. The
5 communication media includes, but not limited to, wired or wireless
methodologies implemented with an electrical, optical, RF,
infrared, acoustic, microwave, Bluetooth, or other transmission
media.
[0047] The input device(s) 410 may include, but not limited to,
10 a keyboard, mouse, pen, joystick, trackball, a voice device, a
scanning device, touch screen or any another device that is capable
of providing input to the computer system 402. In an embodiment
of the present invention, the input device(s) 410 may be a sound
card or similar device that accepts audio input in analog or
15 digital form. The output device(s) 412 may include, but not limited
to, a user interface on CRT or LCD, printer, speaker, CD/DVD
writer, or any other device that provides output from the computer
system 402.
[0048] The storage 414 may include, but not limited to, magnetic
20 disks, magnetic tapes, CD-ROMs, CD-RWs, DVDs, flash drives or any
other medium which can be used to store information and can be
accessed by the computer system 402. In various embodiments of the
present invention, the storage 414 contains program instructions
for implementing the described embodiments.
25 [0049] The present invention may suitably be embodied as a
computer program product for use with the computer system 402. The
method described herein is typically implemented as a computer
program product, comprising a set of program instructions which
is executed by the computer system 402 or any other similar device.
30 The set of program instructions may be a series of computer
readable codes stored on a tangible medium, such as a computer
readable storage medium (storage 414), for example, diskette, CD-ROM, ROM, flash drives or hard disk, or transmittable to the
computer system 402, via a modem or other interface device, over
either a tangible medium, including but not limited to optical or
analogue communications channel(s) 408. The implementation of the
5 invention as a computer program product may be in an intangible
form using wireless techniques, including, but not limited to,
microwave, infrared, Bluetooth, or other transmission techniques.
These instructions can be preloaded into a system or recorded on
a storage medium such as a CD-ROM or made available for downloading
10 over a network such as the internet or a mobile telephone
network. The series of computer readable instructions may embody
all or part of the functionality previously described herein.
[0050] The present invention may be implemented in numerous ways
including as a system, a method, or a computer program product
15 such as a computer readable storage medium or a computer network
wherein programming instructions are communicated from a remote
location.
[0051] While the exemplary embodiments of the present invention
are described and illustrated herein, it will be appreciated that
20 they are merely illustrative. It will be understood by those
skilled in the art that various modifications in form and detail
may be made therein without departing from or offending the scope
of the invention.
We claim:
1. A system (100) for developing unified digital platform based
virtual power banks, the system (100) comprises:
a memory (112) storing program instructions; and
5 a processor (110) executing program instructions stored in the
memory (112) and configured to:
derive a second data type by analyzing one or more record
types, wherein the record types are obtained from a first data
type received from multiple sources and stored in a database
10 (102);
generate virtual power banks by employing the first data
type and the second data type fetched from the database (102),
wherein the virtual power banks are associated with one or more
attributes relating to power management and settlement;
15 generate one or more dynamic actionable items relating to
the virtual power banks from the first data type and the second
data type;
identify one or more variables that correspond to
different types of the dynamic actionable items for
20 categorizing the dynamic actionable items based on the
identified variables; and
perform optimization operations on values of each of the
identified variables to obtain an optimized final weightage
value of the virtual power banks, accessed via a unified digital
25 platform (106a), based on which one or more operational
parameters associated with the virtual power banks are
determined.
2. The system (100) as claimed in claim 1, wherein the first data
type represents data relating to power usage, requirements
associated with multiple sources, data related to real-time
power supply and demand received from one or more utilities
5 and data related to multiple variables which are stored in the
database (102), the multiple sources comprise renewable energy
generators, utility retailers, end-consumers, and Electric
Vehicle (EV) charging stations.
10 3. The system (100) as claimed in claim 1, wherein the one or more
record types comprise Power Purchase Agreement (PPA) records
associated with the renewable energy generators, Renewable
Purchase Obligation (RPO) records associated with utility
retailers, Power Bank (PB) contract records associated with
15 end-consumers, and billing and payment records associated with
EV charging stations.
4. The system (100) as claimed in claim 1, wherein the system
(100) comprises a data analytics unit (104) executed by the
20 processor (110) and configured to derive the second data type
based on one or more functionalities comprising enterprise
resource planning, PPA and RPO record management, PB contract
management, contract and transaction management, and endconsumer data management.
25
5. The system (100) as claimed in claim 1, wherein the second data
type comprises data related to power usage, a quantum of power
to be supplied, duration of supply per terms of usage of the
virtual power banks, negotiated price of the virtual power
30 banks, penalties for non-compliance related to power supply and
usage per RPO, data related to end-consumer segments, and
sources of power generation.
6. The system (100) as claimed in claim 1, wherein the attributes
associated with the virtual power banks comprise different
quantities of power (in kWh), time range for power utilization
and price associated with the power based on the sources of
5 renewable energy received from the renewable energy generators.
7. The system (100) as claimed in claim 1, wherein the virtual
power banks employ end-to-end encryption with blockchain
technology containing smart contracts for use by end-consumers,
10 utility retailers and renewable energy generators.
8. The system (100) as claimed in claim 7, wherein the virtual
power banks are operated based on four types of smart contracts
including a first type of smart contract relating to a PPA
15 between the renewable energy generator and the utility retailer
for trading renewable energy capacity, a second type of smart
contract relating to a PPA between the utility retailer and
the end-consumer for trading the virtual power banks, a third
type of smart contract relating to a PPA between the end20 consumer and an EV retailer for transfer of energy for charging
of EV vehicles, and a fourth type of smart contract relating
to a peer-to-peer contract between the end-consumer with other
end-consumers or with retailers in selling their unused virtual
power banks or self-generated renewable energy power or power
25 stored in batteries of the EV vehicles.
9. The system (100) as claimed in claim 8, wherein the first type
of smart contract has a unique hash key function, which is
generated between the utility retailors and the renewable
30 energy generators and provides one or more predetermined
conditions comprising, quantity, price, and timeline of
generated power.
10. The system (100) as claimed in claim 9, wherein the first type
of smart contract is bifurcated into multiple sub-contracts
which are associated with the virtual power banks and are hosted
in a cloud platform, and wherein the sub-contracts have unique
5 hash functions that ensure end-to-end encryption of the first
type of contract, and wherein the hash function is backtracked
for generating the first type of smart contract, each subcontract has a transaction appending a buyer or a seller token
ID.
10
11. The system (100) as claimed in claim 1, wherein the dynamic
actionable items that are generated from the first data type
includes data related to real-time power supply and demand
received from one or more utilities and data related to multiple
15 variables, and the second data type fetched from the database
(102).
12. The system (100) as claimed in claim 1, wherein the dynamic
actionable items comprise a segmented actionable item, a time20 based actionable item, a peak actionable item, a penetration
actionable item, a competitive actionable item, and a bulk
actionable item, and wherein the multiple variables include
season or weather, landscape conditions, current market price,
frequent purchaser data, forecasted market price.
25
13. The system (100) as claimed in claim 1, wherein the system
(100) comprises a virtual power bank generation unit (106)
executed by the processor (110) and configured to generate the
dynamic actionable items by employing the first data type and
30 the second data type based on a sequence of steps comprising:
determining a base value relating to a power procurement
value between an end-consumer and a renewable energy generator based on a PPA between a utility and a renewable
energy generator;
determining a utility value associated with
5 infrastructure usage allowance based on which a threshold
value is determined, wherein the threshold value
represents a minimum value below which the virtual power
banks cannot be operated; and
determining an optimized weightage value of the virtual
10 power banks based on the first data type and the second
data type by initially identifying and processing the one
or more multiple variables and to determine one or more
sub-variables associated with each of the multiple
variables.
15
14. The system (100) as claimed in claim 13, wherein the virtual
power bank generation unit (106) computes an initial weightage
value for each of the sub-variables, and carries out a first
optimization operation for optimizing the computed initial
20 weightage values for each of the sub-variables, and wherein
the first optimization operation is carried out by employing
machine learning and deep learning techniques to train a model
iteratively that results in a maximum and minimum function
evaluation.
25
15. The system (100) as claimed in claim 13, wherein the virtual
power bank generation unit (106) captures data associated with
the sub-variables for the end-consumer who accesses the
unified digital platform (106a) for obtaining the virtual power
30 banks, and wherein the virtual power bank generation unit (106)
analyzes interdependency between each sub-variable with respect
to another sub-variable based on the captured data in a matrix
form across rows and columns of the matrix.
16. The system (100) as claimed in claim 14, wherein the virtual
power bank generation unit (106) adds the initial weightage
values of the interdepending sub-variables based on the
interdependency analysis and subsequently assigns a label to
5 each of the sub-variables, and wherein the labels associated
with each sub-variable are replaced with a numerical value.
17. The system (100) as claimed in claim 16, wherein the virtual
power bank generation unit (106) carries out a second
10 optimization operation for optimizing the initial weightage
values by computing an average of the numerical values assigned
to each of the sub-variable in either of each row or column of
the matrix to generate a first weightage value for each of the
variables.
15
18. The system (100) as claimed in claim 17, wherein the virtual
power bank generation unit (106) replaces the identified
variables with one or more computed final weightage values
corresponding to each of the variables, and wherein the virtual
20 power bank generation unit (106) carries out a third
optimization operation by computing an average of the final
weightage values for each of the variables associated with the
dynamic actionable items for each row of a matrix.
25 19. The system (100) as claimed in claim 18, wherein the virtual
power bank generation unit (106) determines a maximum average
value from the computed final weightage average values
associated with all the dynamic actionable items across each
column of the matrix to determine a second weightage value,
30 and wherein the second weightage value is the optimized final
weightage value of the virtual power banks.
20. The system (100) as claimed in claim 19, wherein the virtual
power bank generation unit (106) determines the operational parameters for the virtual power banks by carrying out a
multiplication operation between the computed second weightage
value and the threshold value, and then adjusting against the
base value.
5
21. The system (100) as claimed in claim 1, wherein the system
(100) comprises a visualization unit (108) executed by the
processor (110) and configured to render a Graphical User
Interface (GUI) on a user device (116) for providing
10 visualization functionalities comprising a dashboard, an
application, and a portal for end-consumers to access the
unified digital platform (106a) for operating or generating the
virtual power banks.
15 22. The system (100) as claimed in claim 21, wherein the
visualization unit (108) provides a functionality for tracing
the renewable energy source used to generate the power
obtainable as virtual power banks, and wherein the
visualization unit (108) provides a functionality of
20 determining the unused power associated with the end-consumer
for re-using by listing the end-consumers having unused power.
23. A method for developing unified digital platform based virtual
power banks, the method is implemented by a processor (110)
25 configured to execute instructions stored in a memory (112),
the method comprises:
deriving a second data type by analyzing one or more
record types, wherein the record types are obtained from a
first data type received from multiple sources and stored in a
30 database (102);
generating virtual power banks by employing the first
data type and the second data type fetched from the database
(102), wherein the virtual power banks are associated with one or more attributes relating to power bank management and
settlement;
generating one or more dynamic actionable items relating
to the virtual power banks from the first data type and the
5 second data type;
identifying one or more variables that correspond to
different types of the dynamic actionable items for
categorizing the dynamic actionable items based on the
identified variables; and
10 performing optimization operations on values of each of
the identified variables associated with the dynamic actionable
items to obtain an optimized final weightage value of the
virtual power banks, accessed via a unified digital platform
(106a), based on which one or more operational parameters
15 associated with the virtual power banks are determined.
24. The method as claimed in claim 23, wherein the record types
are analyzed to derive the second data type based on one or
more functionalities comprising enterprise resource planning,
Power Purchase Agreement (PPA) and Renewable Purchase
20 Obligation (RPO) record management, Power Bank (PB) contract
management, contract and transaction management, and endconsumer data management.
25. The method as claimed in claim 23, wherein the virtual power
25 banks are operated based on four types of smart contracts, and
wherein a first type of smart contract relates to PPA between
a renewable energy generator and a utility retailer for trading
renewable energy capacity, a second type of smart contract
relates to a PPA between the utility retailer and an end30 consumer for trading the virtual power banks, a third type of
smart contract relates to a PPA between the end-consumer and an EV retailer for transfer of energy for charging of EV
vehicles, and a fourth type of smart contract relates to a
peer-to-peer contract between consumers with other endconsumers or with retailers in selling their unused power banks
5 or self-generated renewable energy power or power stored in
batteries of the EV vehicles.
26. The method as claimed in claim 25, wherein the first type of
smart contract has a unique hash key function, which is
10 generated between the utility retailors and the renewable
energy generators and provides one or more predetermined
conditions comprising, quantity, price, and timeline of
generated power.
15 27. The method as claimed in claim 26, wherein the first type of
smart contract is bifurcated into multiple sub-contracts which
are associated with the virtual power banks and are hosted in
a cloud platform, and wherein the sub-contracts have unique
hash functions that ensure end-to-end encryption of the first
20 type of contract, and wherein the hash function is backtracked
for generating the first type of smart contract, each subcontract has a transaction appending a buyer or a seller token
ID.
25 28. The method as claimed in claim 23, wherein the dynamic
actionable items are generated from the first data type that
includes data related to real-time power supply and demand
received from one or more utilities and data related to multiple
variables, and the second data type.
30
29. The method as claimed in claim 28, wherein the dynamic
actionable items comprise a segmented actionable item, a timebased actionable item, a peak actionable item, a penetration
actionable item, a competitive actionable item, and a bulk actionable item, and wherein the multiple variables include
season or weather, landscape conditions, current market price,
frequent purchaser data, forecasted market price.
5 30. The method as claimed in claim 23, wherein the dynamic
actionable items are generated by employing the first data type
and the second data type based on a sequence of steps
comprising:
10 determining a base value relating to a power procurement
value between an end-consumer and a renewable energy
generator based on a PPA between a utility and a renewable
energy generator;
determining a utility value associated with
15 infrastructure usage allowance based on which a threshold
value is determined, wherein the threshold value
represents a minimum value below which the virtual power
banks cannot be operated; and
20 determining an optimized weightage value of the virtual
power banks based on the first data type and the second
data by initially identifying and processing the one or
more variables to determine one or more sub-variables
associated with each of the multiple variables.
25
31. The method as claimed in claim 30, wherein the step of
performing optimization operations comprises computing an
initial weightage value for each of the sub-variables, and
carrying out a first optimization operation for optimizing the
30 computed initial weightage values for each of the sub-variables
by employing machine learning and deep learning techniques to
train a model iteratively that results in a maximum and minimum
function evaluation.
32. The method as claimed in claim 31, wherein data associated with
the sub-variables is captured for the end-consumer who accesses
the unified digital platform (106a) for obtaining the virtual
power banks, and wherein interdependency between each sub5 variable is analyzed with respect to another sub-variable based
on the captured data in a matrix form across rows and columns
of the matrix.
33. The method as claimed in claim 31, wherein the initial weightage
10 values of the interdepending sub-variables are added based on
the interdependency analysis and subsequently a label is
assigned to each of the sub-variables, and wherein the labels
associated with each sub-variable are replaced with a numerical
value.
15
34. The method as claimed in claim 33, wherein the step of
performing optimization operations comprises carrying out a
second optimization operation for optimizing the initial
weightage values by computing an average of the numerical
20 values assigned to each of the sub-variable in either of each
row or column of the matrix to generate a first weightage value
for each of the variables.
35. The method as claimed in claim 34, wherein the step of
25 performing optimization operation comprises replacing the
identified variables with one or more computed final weightage
values corresponding to each of the variables, and carrying
out a third optimization operation by computing an average of
the final weightage values for each of the variables associated
30 with the dynamic actionable items for each row of a matrix.
36. The method as claimed in claim 35, wherein the step of
performing optimization comprises determining a maximum average
value from the computed final weightage average values associated with all the dynamic actionable items across each
column of the matrix to determine a second weightage value,
and wherein the second weightage value is the optimized final
weightage value of the virtual power banks.
5
37. The method as claimed in claim 36, wherein the operational
parameters for the virtual power banks are determined by
carrying out a multiplication operation between the computed
second weightage value and the threshold value, and then
10 adjusting against the base value.