Field of the Invention:
[001] In general, this invention focuses on Smart Grid optimization and energy management. The
traditional techniques are mainly switch operated and manual. All the calculations are performed
manually and make the decision accordingly. It is much more significant to multiple components
and methods to enhance energy supply and demand supervision in individuals and industries.
Background of the Invention:
[002] The requirement for energy efficiency and consumption has grown as global energy demand
has expanded under a challenge from environmental concerns and fluctuating energy prices. Also,
with home automation, users can configure their heating and cooling systems to use less energy
while away from home or asleep. Homes, offices, and other buildings can now use solar panels, fuel
cells, wind turbines, backup generators, and other energy sources. However, due to challenges in
cost recovery, and the unpredictable nature of solar and wind power, the complexity of integrating
will be increased.
[0003] Generally, electric utilities have set up special devices in residential and commercial
applications that, when remotely triggered by the utility, cut power to certain appliances during peak
hour scenarios. Customers that agree to install these devices are rewarded with rebates or other
incentives, and the utility can more effectively control the demand for energy from a distance.
However, these agreements are spontaneous and bound customers to the power company's moods.
[0004] Depending on circumstances, different electric utilities charge different prices. For instance,
the cost of electricity increases at times of peak demand. On the other hand, a reduced rate may be
applied since there is little demand. In recent years, regulators have also encouraged utilities to
repurchase electricity from customers who can produce more than they require. The incapacity of
certain energy users to reduce their energy use and the lack of real-time data regarding the current
cost of energy usage are two factors that have contributed to the limited effectiveness of such
programs.
3
[0005] However, these technologies may include additional energy management programs like load
shedding and peak demand limits, and the present invention is focused explicitly on load shifting. To
optimize the energy consumption at any precise point during the cycle, divide the off intervals
throughout the load shifting schedule. Therefore, it is undesirable to have all of the loads on at once.
Although load balancing has been done in the prior art, prior art load balancing systems did not
attempt to spread the off intervals uniformly throughout the intermission so that there is a uniform
gap time among orthogonal directions. These systems also ignore de-energize the loads by first deenergizing the load with the most significant off-time watt value, the load with the smallest off-time
watt value, then the load with the next most enormous off-time watt value, and so on.
[0006] Our system extends this content information to extended smart system in such an order that
analytics can be performed over the existing in an efficient manner.
Summary of the invention:
[007] The proposed smart framework is an amalgamation of three major components. The
framework consisting renewable energy sources, power quality, and demand response. The
framework configured with the constraints. Power availability is an essential factor for the smart
environment i.e., Smart city, Smart Building etc. Whenever thinking about the life of devices, an
important factor comes to the picture and that factor is quality. A quality power is enough to enhance
the device's life and operation efficiency. It is very challenging task to draft the power module for
smart environment. This study proposes a smart optimization framework to integrate the power
quality with the smart framework. All equipment’s are fully utilized and make the grid stable in any
circumstances. The system includes multiple loads and is divided into three categories, i.e. Critical,
Reschedulable, and Curtailable load. The IoT environment enables the system to perform multiple
activities in the form of multithreads. The IEEE dataset is used to implement the power quality
model, and Dataset for integrated energy sources is imported from Data Repository for power. The
proposed framework is evaluated with a setup of hyperparameters.
4
Objectives of the present invention:
[0008] Primary objective of the present invention is to provide an integrated framework for smart
grid which includes the non-conventional energy sources along with existing grid system.
[0009] Another objective of the present invention is to integrate the quality model with energy
sources. Before providing the electricity to the device, it needs to equal the rated voltage profile
as requirement.
[0010] The further objective of the present invention is to cover the load optimization and provide
electricity at the cheapest price.
5
Detailed Description of the invention:
[0011] Demand of energy has increased day by day cumulatively. The number of
devices/equipment’s is exponentially increasing in today’s hex. The ministry of power has proposed
the National electricity ten-year action plan to supply the electricity across the country. The
proposed plan ensures the supplied power is efficient or not. According to the world resource
institute India produces approx. 6.7% carbon emission. As per the prediction of the world energy
council, the peak of electricity will be attained by 2030.
[0012] The energy is collected from various resources which are available naturally like Wind,
Wave, Tide, Solar, etc. The energy collected from these sources is converted into the desired energy
with the help of transducers and electronic circuits. A photovoltaic or solar cell, where the sunlight is
fallen on the P-N junction, has a voltage across this junction. It has a two-layer structure consisting
of a P-type semiconductor, known as the base, N-type semiconductor, known as the Emitter. On the
cell's periphery, an anti-reflection coating is coated so that incident light does not reflect. Metals
contacts are provided at the surface for the electrical connection, depending on crystal solar cell
efficiency described below in the form of a table.
Table 1: Solar Cell Efficiency
Sr. No Material Efficiency
1. Mono crystalline silicon cell 15% - 18%
2. Poly crystalline silicon cell 14% - 16%
3. Amorphous silicon cell 6% - 8%
There should be a value for the wind. The generation of the power is performing at a specific wind
speed. Below the rated speed, no power output will be obtained. The max power is obtained from
wind power generation. There are some limits of any system, while collecting the energy from wind
turbine there is a limit of power generated from wind power system.
0 ≤ 𝑃𝑊(𝑡) ≤ 𝑃𝑊
𝑀𝑎𝑥(𝑡) (1)
Where Pw is wind energy and upper limit of generation from this source is Pmax w.r.t. time. In
particular, when renewable energy sources are included, the available power sources for power
generation on a per-grid basis may differ due to environmental factors, so it is necessary to
integrate, and operate them.
6
[0013] Integrating energy (solar, wind, hydro, Storage System, etc.) is improving Smart Grid's
capability to fulfill the demand. Integration is applied or summing up the different energy sources;
some challenges like frequency and waveforms have arisen. Furthermore, intelligent technologies
are used to overcome such challenges to make the system intelligent, efficient, secure, and stable.
The system may be identifying the brownout periods due to the unexpected availability of solar and
wind [15,47,49]. However, the present research focused on the sophisticated framework, algorithm,
and model to reduce blackout, brownout, and energy costs.
Maximum Demand=
𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝐿𝑜𝑎𝑑 𝑥 𝐿𝑜𝑎𝑑 𝐹𝑎𝑐𝑡𝑜𝑟
𝑃𝑜𝑤𝑒𝑟 𝐹𝑎𝑐𝑡𝑜𝑟
(2)
[0014] The traditional grid is one-way communication, but the smart grid emphasizes adopting smart
technology. The internet-based smart grid ensures the power quality and availability of the power.
The electrical grid is a network of electrical generation, transmission, and distribution, like the
internet, a network of networks. The internet-enabled smart grid makes it easy to monitor, control &
operate the grid functions.
[0015] Most of the appliances were manual and followed basic automation control system
techniques. Nevertheless, in today’s hex, the Internet of Things has become an excellent evolution
for making systems smart and automated. Several information and communication technologies in
the grid make the grid operation fast, secure, and efficient. The electrical energy converted from
renewable, nuclear, fossil fuels, and other sources can be mapped with the help of energy
management tools, as described in figure 1.
Programm
able Logic
Controller
SCADA
Building
Manageme
nt System
Figure 1: Energy Management Tools
7
Experimental Setup
[0016] The experimental setup is nothing but summarizes the data, constraints, and environment
variables taken while setting up the experiment. During the experiment setup, it is required to
identify the control variables, parameters, and standard frequencies. Such type of parameters is
described below:
[0017] Data Parameters/Configuration:The module configuration is high end setting, and without
these settings system is under red flag and idle condition.
[0018] Control Variables: The control variable is Ci, directly sending the instruction to the work
storage End-use i for the operational module. It is the dimension of z where ith devices and flag zero
when the device under the idle condition at time-t. When the flag one at time interval-t, it is
considered an appliance completed its assigned task.
Table 2: Environment Setup
User Preference Units Description
Ci,t 0.1 end-use operations, i statement, tinterval
Environment Data
Pt Inr/kWh t- interval price
NLt kWh Non-Shiftable-load, t- interval
At kWh Energy Cap for interval t
End-Use Characteristic
zi - End-use, i for Number of intervals
Ii kWh Average, End-use for i. active
Mode
8
[0019] The constraints for the interconnected energy sources for the proposed framework are given
below:
Table 3: Constraints
Sr. No. Source/Resource Constraints
1. Solar 0 ≤ 𝐸𝑃𝑉 ≤ 𝐸𝑃𝑉
𝑀𝑎𝑥(𝑡)
𝑃𝑃𝑉 ≤ 𝐴𝑃𝑉 ∗ 𝑓 ∗ 𝑠𝑓(𝑡)
2. Wind 0 ≤ 𝑃𝑊(𝑡) ≤ 𝑃𝑊
𝑀𝑎𝑥(𝑡)
𝑃𝑊 = 0 if 𝑢𝑓 < 𝑢𝑐𝑖&𝑢𝑓 > 𝑢𝑐𝑜 u↔v
𝑃𝑤 = 𝑃𝑟𝑎𝑡𝑒𝑑 if 𝑢𝑟 ≤ 𝑢𝑓 ≤ 𝑢𝑐𝑜
𝑃𝑊 = 𝑃𝑟𝑎𝑡𝑒𝑑 *
𝑢𝑓−𝑢𝑐𝑖
𝑢𝑟−𝑣𝑐𝑖
if 𝑢𝑐𝑖 ≤ 𝑢𝑓 ≤ 𝑢𝑟
3. Smart grid
𝐸𝐺𝑟𝑖𝑑 + 𝐸𝑃𝑉 +𝐸𝑊 + ∑ 𝐸𝐸𝑉
𝐷𝑖𝑠𝑐ℎ
𝑁𝐸𝑉
𝑖
(𝑡, 𝑖)+ 𝐸𝐵
𝐷𝑖𝑠𝑐ℎ
(𝑡)
4. Electric Vehicle
𝑃𝐸𝑉
𝐶𝐻 ≤ 𝑃𝐸𝑉
𝐶𝑚𝑎𝑥 ∗ 𝑤(𝑡, 𝑖) ; ∀ t ∈thome
𝑃𝐸𝑉
𝐶𝐻(𝑡, 𝑖)=0; ∈ t ≠ thome
5. Storage/Inverter 𝑃𝐵
𝐶𝐻 < 𝑃𝐵
𝐶𝑚𝑎𝑥 ∗ 𝑦(𝑡)
𝑃𝐵
𝐷𝑖𝑠𝑐ℎ ≤ 𝑃𝐵
𝐷𝑚𝑎𝑥 ∗ 𝑧(𝑡)
[0020] Where Epv is indicating the photovoltaic energy, E
max
-upper bound of solar. Apv- is average
power and can vary between the defined parameters. Pw-wind power, u-density of air. Energy
generated by wind w.r.t. time is equal to rotor area. The smart grid constraint represents the power
generated by other conventional energy sources. The electric vehicle’s constraints open for future
plugins to transfer the power for an electric vehicle. PCH
-indicating the charging of storage and
P
Disch
-notation for discharging of storage in case faults, energy sources failures.
Framework
[0021] The smart framework integrates the quality model and integrated renewable energy sources.
This framework integrates the models and provides a comfortable platform for concord between
them.
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Input: Fitness Function, Constraints, Load Population, Population Ratings
1. Initialize Renewable Energy Sources along with Grid, Weather Forecasting, Sensors, Power Quality Model
2. Calculate incoming from Renewable Source, affordable load and Display
3. If Source_IncomingRated_Voltage
RB = ON;
Flag = 1;
Else
Flag=1;
End If
4. If Flag==1;
Switch(D)
Case 1:
S + w ≥ D
→Satisfy the load
Case 2:
S + w < D
→Connect Grid
Case 3:
A_P == 0
→Apply GA
→Population = Population [] – GA(Result)
Case 4:
→Storage ON for Grid Internal Circuitry
End Switch
End If
5. Synchronize
Calculate:
Maximum Demand = (Connected Load x Load Factor)/(Power Factor)
X= Demand – Supply;
Stop
Note:
S: Solar
w: wind
A_P: All Power
D: Demand
CB: Capacitor Bank
GA: Genetic Algorithm
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No
No
Vo
lta
ge
=
Ra
ted
Capacitor Bank
Voltage < Rated
Reactor Bank Voltage > Rated Voltage Rating
RERs, Grid,
Sensors
Load, Cost per
Unit
Optimization
Constraints and
Fitness function
Power Quality Model
Calculate Demand & Load Ratings
Obtain sun and wind availability
Satisfy the Load
Yes
S + W ≥ D
Connect Grid
with Base Load
(S+W) < D Yes
(A_P < D
A
_P
=
Storage / Inverter ON 0
+
Internal Load/ Grid ON
Yes
Stop
Fitness Function
Selection
Crossover
Mutation
Stop
Criteria
No
GA
Ye
s
Initialization
Figure 2: Flowchart of Proposed Framework
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[0022] The schematic diagram indicates the used elements and their connection preferences. This
framework considered the solar and wind energy sources to represent the renewable energy sources,
and the grid represents the energy generated by fossil fuels or other sources. All the energy sources
are integrated and connected with the proposed programmable framework. There is also a storage for
the grid internal circuitry troubleshooting, fault, and route locating to the targeted load. The
framework is bounded by the constraints of energy sources and load parameters for making the grid
smart, stable, and without a saddle point. The load is allotted in three categories i.e., Critical load,
Reschedulable Load, and Curtailable Load. The Critical load is essential and must be ON in all
circumstances. However, the reschedulable load is shiftable and shifts to available local energy
sources. At the same time, the Curtailable load is fancy and not configured to connect with the
system during peak hours. The disconnected load has shifted in a queue, and the sorting algorithm
sorts the load according to their preference. The sorted load shifts to the ready queue for connecting
the load again. In order to optimize the load, the genetic algorithm is used. The result of the genetic
algorithm is disconnected from the load population array, and update the status of load for
calculating the demand of the connected loads.
Figure 3: Schematic Diagram of Proposed Framework
12
Figure 4: Experiment Results
Figure 5: Comparative Analysis
13
Table 4: Cost Saving Comparison
Appliance
Without Framework
Per Day (Rupees)
With Framework
Per Day (Rupees) Saving (%)/Day
4-Hours Per Day 4 Hours Per Day
SA-I 3.5 21 3.1 18.6 11.4
SA-II 3.72 22.32 3.55 21.3 4.56
SA-III 3.43 20.58 3.25 19.5 5.24
SA-IV 3.65 21.9 3.42 20.52 6.30
Discussion
[0023] The simulation results show the various variables output, i.e., consumption, scheduling, cost,
and power quality. The results clearly show the framework's performance. There is no breakdown in
the scheduled simulation, and all the framework elements are fully utilized.
[0024] The above waveform represents the output power received from the different energy sources,
i.e., solar, wind, and other fossil fuels. The continuous wave represents the carried experiment has no
breakout, and system works smoothly. The power received from solar has less harmonic distortion,
so harmonic deduction devices are ignored for the deduction of solar harmonics. The frequencies of
all sources are considered to 50 Hz with a ±5%. The development of the integration of genetic
algorithm along with framework shows excellency and efficiency in terms of coding simplicity, fast
convergence speed, and accuracy. To minimize the operating cost of a grid system considering the
line constraints and renewable energy sources. This frame validates the results. Moreover, it is also
remarking the proposed framework's superiority, efficiency, and stability. This analysis performs
against the three parameters i.e., power consumption, cost, and quality.
[0025] The comparison chart shows that using a genetic algorithm, the device's power consumption
is optimized. The scenario represents the device usage in a day. The timeline is in hours (0-24). It
indicates the best result is provided during its peak hours. It increases the availability and reliability
of the smart grid. It is affected the curtailable and shiftable load. It plays a crucial role when the load
increases during peak hours.
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[0026] The savings on electricity price and improvement in system load factor greatly depend on
various factors, including real-time pricing, weather, the flexibility of loads, set-points, available
control equipment in the building and distribution grid, and accuracy/availability of the forecasts.
Real-time pricing and weather variations also affect the customer’s cost savings and system load.
Optimization of operating cost under real-time pricing, and time of use (TOU) has applied to analyze
the optimal values and compare them with the optimal values of various existing frameworks.
Advantages of the present invention:
[0027] The following are the technical advantages of the present invention over the prior are as
disclosed above:
1. Utilize all resources and improve the efficiency of Smart Grid
2. Diversify the energy supply, reducing carbon emission, cut downs, and the dependencies on
imported fuels.
3. The renewable energy is cheaper, efficient, reliable than other sources except initial cost.
4. This kind of technology create new jobs.
5. Enables possibility of low latency on demand analytics due to internet of things.
[0028] Although the presented subject matter has been discussed in detail, any restrictions that
may emerge from this is not intended. Numerous functioning alterations to the approach may be
made to execute the visual identity as described, as a practitioner of the technique would
comprehend.

We Claim:

1. An integrated system of renewable technology and power quality comprises of:
a. Integration of Renewable Energy Sources: A framework which is integrated all
renewable energy sources along with maintaining of frequency. The framework
makes important signals and predict the availability of power from renewable energy
sources. It also generates more abstract and generalised analytics for future
recommendations.
b. Integration of Power Quality: It is composed of quality of power if the rating of
power is less or more than the rated power from appliance manufacturing rating, then
this framework does not process the power for further processing, and it is secure the
appliances from running out of order.
c. Energy Management: Most of the appliances were manual and followed basic
automation control system techniques. Nevertheless, in today’s hex, the Internet of
Things has become an excellent evolution for making systems smart and automated.
Several information and communication technologies in the grid make the grid
operation fast, secure, and efficient. The electrical energy converted from renewable,
nuclear, fossil fuels, and other sources can be mapped with the help of energy
management tools
2. An integrated initialization process of smart framework comprises of:
a. Initialize all sensors, renewable energy sources, and weather forecasting
b. Check the signal from power quality module.
c. Calculate the available power and connected load.
d. Either the load profile is equal to available power check the real time market for decide
the tariff plans.
3. An integrated process for stability of smart framework comprises of:
a. Check the Supply-demand ratings if match then connect
b. If only Renewable energy sufficient to satisfy load, then connect.
c. If power is less then demand connect grid and satisfy load.
d. If all power zero then resultant of genetic algorithm deducts from the load population,
and store the deducted load in a waiting queue.
e. The storage connects only when unexpected circumnutates occurs