Smart Energy Management in Virtual Power Plant Paradigm with A New
Improved Multilevel Optimization Based Approach
OBJECTIVE
The main objective of this project is to produce electrical energy from
different type of sources, load control and sensor monitor with help of IOT
technology.
INTRODUCTION
Due to increase in energy demands of the world, sustainable energy and
economic growth are imperative for survival of the industry. However, lack of
efficient government policies, rapid increase in demand and shortage of electricity
has caused a major energy crisis in numerous countries. Emissions due to use of
conventional sources, losses in transmission, and power theft have worsened the
situation. To improve the present scenario, virtual power plants (VPPs) have been
Introduced in the industry. A VPP is a cloud based distributed power plant that
aggregates the capacities of diverse distributed energy resources (DERs) to
enhance power generation, and trade or sell power on the electricity market. It is an
interconnected network of decentralized and medium scale power generating units,
flexible power consumers and storage systems dispatched through a central control
room. However, they remain independent in their operation and ownership. The
goal of a VPP is to relieve load on the grid during peak hours by smartly
distributing the power generated by individual units. This enables the hub to
efficiently monitor, coordinate and control all assets. Control commands and data
transmit via secure data connections, shielded from other data traffic due to
encryption protocols. The central control system operates every individual asset
along an optimized schedule.
ABSTRACT:
A virtual power plant (VPP) is a cloud based distributed power plant that
aggregates the capacities of diverse distributed energy resources (DERs) for the
purpose of enhancing power generation as well as trading or selling power on the
electricity market. The main issue faced while working on VPPs is energy
management. Smart energy management of a VPP is a complex problem due to the
coordinated operation of diverse energy resources and their associated
uncertainties. This research paper proposes a real- time (RT) smart energy
management model for a VPP using a multi-objective, multi-level optimizationbased approach. The VPP consists of a solar, wind and thermal power unit, along
with an energy storage unit and some flexible demands. The term multi-level refers
to three different energy levels depicted as three homes comprising of different
amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional
communication infrastructure. Multi-objective RT smart energy management is
implemented on a community-based dwelling system using three alternative
algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm
optimization (H-PSO) and advanced multi-objective grey wolf optimization
(AMO-GWO). The proposed technique focuses on achieving the objectives of
optimal load scheduling, real-time pricing, efficient energy consumption, emission
reduction, cost minimization and maximization of customer comfort altogether. A
comparative analysis is performed among the three algorithms in which the
calculated real-time prices are compared with each other. It is observed that on
average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO
performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper
concludes that AMO-GWO is the briskest, most economical, and efficient
optimization algorithm for RT smart energy management of a VPP.
EXISTING SYSTEM
 In this existing system produce electrical power from solar and dynamo rotation
based method only.
 In this old project is used for Bluetooth, ZIGBEE, RF based communication
device and not available IOT based communication method. Distance coverage
problem occurred in this model.
 Poor power management for in this system.
PROPOSED SYSTEM
 In the proposed system, microcontroller is connected with voltage sensor, LCD
display, inverter, Driver Circuit with Relay and IOT server.
 The thermal energy harvester is attributed to the heat applied on two different
metals. The temperature difference between them will deliver a potential
voltage directly proportional to temperature difference. This is called the See
beck effect.
 The wind model is convert mechanical energy to electrical energy with help of
air and dynamo.
 Solar panel produce electricity from sun light.
 This power saves in battery backup with help of boosting circuit.
 Use of embedded technology makes this system efficient and reliable. Micro
controller (ARDUINO) allows dynamic and faster control. Liquid crystal
display (LCD) makes the system user-friendly to get the voltage. ARDUINO
micro controller is the heart of the circuit as it controls all the functions.
 All sensor value is stored in IOT server. Operate Load depend upon IOT
control key. It is easy to understand the pattern using the Dashboard Controller
interface is implemented using the MQTT protocol.
LITERATURE REVIEW / SURVEY
S.NO
1
Author
Title
of
Year
A.
Internet
things
Abdelgawa
platform for structure health
Work done
(iot) 2017 Analysis
the
IOT
concept.
d and K. monitoring
Yelamarthi
2
Yang
Bai, Hybrid, multi-source and
Heli
2018 Analysis the energy
integrated energy harvesters
harvester’s concept.
Jantunen,
and
Jari
Juuti
3
Manisha R Micro
Mhetre,
energy
2011 Analysis the energy
harvesting
for
harvester’s concept.
Namrata S biomedical applications
Nagdeo
4
K.
An architectural
Yelmarthi,
framework
A.
IOT applications
Abdelgawa
d, and A.
Khattab
for
2016 Analysis
low-power
the
power concept
low-
BLOCK DIAGRAM:
Charge
controller
(SEPIC)
Solar
Power
Voltage
Sensor
Charge
controller
(SEPIC)
Thermal
Source
Voltage
Sensor
LCD
Display
ARDUINO
Micro
IOT
SERVER
ESP8266
Controller
Charge
controller
(SEPIC)
Wind
Source
Relay For
IOT Load
Switch
Battery
12V
DC
LOAD
Voltage
Sensor
Voltage
Sensor
Inverter
AC Load
Power Supply
to All Part
MONITORING AND CONTROL SECTION
ADVANTAGES:
 No pollution
 Easy to implement
 It is very feasible for general facilities that require no large power.
 The proposed system can be simply extended to the green house
environment for Floriculture in office or building sunlight control.
APPLICATIONS:
 It is used in Home and industrial applications.
 It is used in commercial applications.
 It is used in Electrical applications.
HARDWARE REQUIREMENTS:
 ARDUINO controller
 LCD Display
 Power supply unit
 ESP8266 WIFI Modem
 Voltage sensor
 Battery
 Wind model
 PELTIER cell
 Inverter
 SEPIC Converter
 Relay with driver
SOFTWARE REQUIREMENTS:
 EMBEDDED C PROGRAM
 ARDUINO-1.8.13 IDE
 BLYNK IOT SERVER
CONCLUSION
In this paper, an efficient smart energy management frame-work for a
community in a VPP paradigm is designed. After identifying some limitations in
previous work performed in this domain, an intelligent MAS is developed using
JADE platform. Three different algorithms are implemented to improve and
overcome those limitations. These algorithms together achieve the main objectives
of this research that are RTP, optimal load scheduling, minimum energy
consumption, cost minimization, emission reduction and customer comfort. The
proposed technique is applied on three different homes in a community, which are
presented as three different cases. A comparative analysis is performed between HOSR, H-PSO and AMO-GWO in which calculated RTPs are compared with each
other. Energy consumption patterns and device ON/OFF states are also calculated.
Based on RTP, it is observed that on average H-PSO performs 7.86 % better than
H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better
than H-PSO in terms of pricing. Emission rates in kg/kWh are calculated for three
most hazardous GHGs i.e., CO2, SO2 and NO2 and it is observed that the
emissions calculated using AMO-GWO algorithm are the least as compared to
other two algorithms. Hence, it is concluded that on average, the AMO-GWO
algorithm performs better than the other two and provides a more efficient and
optimal solution to the optimization problem. The simulation results provide an
optimum solution to the RT energy management problem of a VPP and validate
the claims that a VPP promotes clean and green energy, efficient energy
consumption, and customer comfort and control.
REFERENCES
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