International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 2 - November 2015
Frequency Control in Solar-Fuel Cell- Diesel Microgrid for
Energy Management
2
1
P. Thangappan, F.Fareeza
M.Tech. Power System Engineering, 2Assistant professor, EEE department
Dr.MGR Educational and Research Institute
Periyar E.V.R. High Road, NH 4 Highway, Maduravoyal, Chennai- 95
1
Abstract—This paper proposes a novel, Frequency
control in a hybrid system for Energy management. Here
Microgrid consists of Solar system, Fuel cell unit and a
diesel unit for frequency control. In this proposed approach
solar output is been controlled and coordinated with other
sources by Neuro-Fuzzy controller. During demand
increase in the network each source in the network feed the
grid according to the priority given and maintains the
frequency of the grid thereby, improving the stability of the
grid. Here solar power is taken as the primary source that
is to be connected during the energy demand. For accurate
coordination control in grid maximum power point is
tracked from solar.
Keywords—maximum power point tracking(MPPT),
Frequency control, Energy Demand, Microgrid, Fuel-Cell
(FC), Diesel Generato.
Introduction
Demand of Energy is increasing in a grid
connected or isolated system. Thus it is very
important to meet the increasing demand. In order to
meet the increasing demand alternative energy
sources for convectional power has to be used i.e.
Renewable energy source such as solar energy, FuelCell, Wind energy, Tidal energy, etc. These sources
are clean and abundantly available in nature, they are
advantageous over convectional energy system such
as low pollution, high efficiency, diversity of fuels,
reusability of exhausts, and onsite installation. The
main objective of energy management is to include
the plan and operation of energy production
and energy consumption units. A Microgrid is a “A
power distribution network comprising multiple
electric loads and distributed energy resources,
characterized by all of the following: a) The ability to
operate independently or in conjunction with a
macrogrid; b) One or more points of common
coupling (PCC’s) to the macrogrid; c) The ability to
operate all distributed energy resources (DER),
including load and energy storage components, in a
controlled and coordinated fashion, either while
connected to the macrogrid or operating
independently. d) The ability to interact with the
microgrid in real time, and thereby optimize system
performance and operational savings”. Fig 1 shows
the typical example of a Microgrid. In this multiple
source system the control schemes will have many
loops in it, so all these has to be controlled and
coordinated in order to maintain the voltage stability
in the system.
ISSN: 2231-5381
Fig. 1 Typical Example of a Microgrid
In this paper, solar-fuel cell-diesel is system is
taken as a microgrid sources. During higher demand
increase, frequency of the network fails
correspondingly. In order to maintain the frequency
stability of the network each source of the microgrid
is added up with the main grid. This addition will
have frequency regulation in the grid which in turn
improves voltage stability.
I.
SYSTEM DESCRIPTION
Fig 2 will describe the block diagram of the
system. Sources such as PV, Fuel Cell, and Diesel
Generator are connected to the main grid through
relays. During higher load demand in the system each
source gets connected to the system to stabilize the
frequency disturbances.
Fig. 2 Block Diagram
A. Solar Power
Solar is the most widely used renewable energy
system used for economical purpose. Though the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 2 - November 2015
varying output from the solar panel is obtained the
impact on the solar power generation is still
necessary to future life. The output power from the
solar panel is made more enhanced high voltage gain
dc to dc converters and MPPT as control algorithm
for the maximum use of the power from the solar
panel.
Fig. 2 Solar Power
B. Fuel-Cell (FC)
FC is a device that converts chemical energy into
electricity through chemical reaction. The fuel cell
must provide competitive, reliable, and quality power
without emitting pollutants such as oxides of
nitrogen, carbon or sulphur. It must respond quickly
to changes in load and have low maintenance
requirements as well as a long cell life. In the
schematic of fuel cell, gaseous fuels are fed
continuously to the anode, and an oxidant i.e.,
oxygen from air, is fed continuously to the cathode
compartment, the electrochemical reactions take
place at the electrodes to produce an electric current.
A fuel cell is individual small unit of around 1.2V. A
group of units are connected in series and in parallel
to get required voltage and current ratings, that group
is called fuel cell stack. Current fuel cells, when
operated alone have efficiencies of about 40-55%.
Fuel cell technology is based upon the simple
combustion reaction (1):
2H2+O2↔2H2O ……………… (1)
C. Diesel Generator
A diesel generator is the combination of a diesel
engine with an electric generator (often an alternator)
to generate electrical energy. This is a specific case
of engine-generator. A diesel compression-ignition
engine often is designed to run on fuel oil, but some
types are adapted for other liquid fuels or natural gas.
Diesel generating sets are used in places where it is
not connected to power grid, or as emergency powersupply if the grid fails, as well as for more complex
applications such as peak-lopping, grid support and
export to the power grid. Sizing of diesel generators
is critical to avoid low-load or a shortage of power
and is complicated by modern electronics,
specifically non-linear loads. A battery bank is the
result of joining two or more batteries together for a
single application. By connecting batteries in series,
ISSN: 2231-5381
parallel & series and parallel, you can increase the
voltage or amperage, or both.
II.
PROPOSED SYSTEM
In the proposed system, microgrid source supplies the
load. During higher demand increase each sources of
the microgrid such as PV, Fuel Cell, Diesel generator
are added for every demand increase without using
external storage. The PV generator is properly
derated to deliver a reduced power output than its
maximum possible value and hence making its output
dispatchable/controllable. The derated amount of
power is kept aside and is used as reserve to supply
the transients whenever required. In addition, this
paper also proposes a novel neural network-based
maximum power point tracking (MPPT) algorithm
for the PV system. Energy management scheme
employs the PV as first, then fuel cell and then Diesel
generator will get added up to attain power balance in
the network. This proposed scheme is implemented
through a central controller.
In this proposed scheme during normal
demand in the system, PV will operate at its
maximum power point, during demand increase other
sources in the microgrid like fuel cell and diesel
generator adds up according to the demand increase,
to maintain the voltage balance in the network.
Here PV is rated for 100 Watts power,
Maximum Power Point Tracking is done by Pertub &
Observe (P&O) based Neuro-Fuzzy algorithm. FuelCell is rated for 230 V, 40Watts power and Diesel
Generator is rated for 215 V.
III.
MPPT
A. PERTURB & OBSERVE
The performance of the proposed tracking
mechanism is validated against the wellaccomplished
perturb and observe (P&O)
mechanism. Fig 3 describes the flow chart of P&O
algorithm. This algorithm is the most common
algorithm because it uses simple parameters for
measurement. In this approach, the module voltage
is periodically given a perturbation and the
corresponding output power is compared with that
at the previous perturbing cycle. In this algorithm a
slight perturbation is introduce to the system. This
perturbation causes the power of the solar module
various. If the power increases due to the
perturbation then the perturbation is continued in
the same direction. After the peak power is
reached the power at the MPP is zero and next
instant decreases and hence after that the
perturbation reverses. When the stable condition is
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 2 - November 2015
arrived the algorithm oscillates around the peak
power point. In order to maintain the power
variation small the perturbation size is remain very
small. The technique is advanced in such a style
that it sets a reference voltage of the module
corresponding to the peak voltage of the module
controlled through the neuro-fuzzy network. Fig 5
describes the Rule Frame Work for Neuro-Fuzzy.
Fig 5. Rule Frame Work for Neuro-Fuzzy
Fig 3. Flow Chart for Perturb & Observe Algorithm
B. NEURO-FUZZY
Neuro-Fuzzy refers to combinations of artificial
neural networks and fuzzy logic. The proposed
scheme utilizes Sugeno-type Fuzzy Inference System
(FIS) controller, with the parameters inside the FIS
decided by the neural-network back propagation
method. The ANFIS is designed by taking speed
error (EN) and change in speed error (d(EN)/dt) as
the inputs. The output stabilizing signals is computed
using the Fuzzy membership functions depending on
these variables. ANFIS-Editor is used for realizing
the system and implementation. Fig 4 describes the
Neuro-Fuzzy frame work
Fig 6. Input layer and Hidden Layers of Neuro-Fuzzy
IV.
SIMULATION RESULT
In order to validate the control strategies the
proposed system is modeled. The simulation result
for the proposed method is analyzed with the
MATLAB software. Simulation is analyzed for the
proposed method. Fig 7 describes the proposed
system SIMULINK model.
Fig 4. Neuro-Fuzzy Frame Work
Rules are framed in neuro-fuzzy for controlling the
supply of sources to be connected during demand
increase and their inverter duty cycle ratio is
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Fig 7. SIMULINK MODEL of Proposed System
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 2 - November 2015
A. MAIN GRID VOLTAGE & CURRENT
Grid source voltage and current are analyzed in this
section. Initially it supplies a voltage of 400 V to the
load when demand of the load increases by time this
result in voltage imbalance in the system; this is
stabilized by adding sources to the load to maintain
the grid voltage. Fig 8 describes the Source Voltage
and Current. Real power of the grid during normal
condition and during demand increase is estimated
and shown in Fig 9.
using P&O based Neuro-Fuzzy. PV output voltage is
shown in Fig. 11 and Real power of the PV is shown
in Fig. 12
Fig 11. PV Output Voltage Variation
Fig 8. Source Voltage and Current
Fig. 12 Real Power Variation in PV
C. FUEL-CELL VOLTAGE & POWER
Fuel-Cell is taken as the second alternate
source of PV during higher demand increase.
Inverter connected will have controlled duty cycle
according to its demand in the network. Fig. 13
shows the Real power variation.
Fig 9. Real Power of the Grid
Fig. 13 Real Power Variation in Fuel-Cell
Fig 10. Frequency of the Grid
During higher demand condition disturbance in
voltage stability occurs, in addition; frequency also
gets disturbed. Due to the coordinated control of the
micro-grid system, frequency is maintained
throughout the system. This can be analyzed from Fig
10.
B. PV VOLTAGE & CURRENT
Here PV panel is the base source during higher
demand increase of load. MPPT is implemented
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It is connected to the main grid during higher
power demand in the network; it also gets
connected during maximum load exceeding the
PV supplied.
D. DIESEL GENERATOR VOLTAGE &
POWER
Diesel Generator is taken as the third alternate
source during higher demand increase for voltage
stability and frequency regulation. Fig. 14 shows
the Real power variation.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 2 - November 2015
E. POWER VARIATION
SOURCES WITH TIME
OF
ALL
THE
Fig.17 describes the power variation of all the
sources during the demand increase in the grid.
Fig. 14 Real Power Variation in Diesel Generator
Fig. 15 describes the load or demand control for
each source in the microgrid as the demand increase
in the network. Source is controlled through neurofuzzy algorithm.
Fig.17 Power variation of Sources
During the higher demand increase in the
network the sources will get added up to the
network, each source addition can be analysed in
Fig 18.
Fig.16 Neuro-Fuzzy Rules for Control of PV source
Neuro-fuzzy control is a method for time-varying and
non-linear processes. In neural fuzzy systems, both
artificial neural network and fuzzy system work
independently from each other. The ANN tries to
learn the parameters from the fuzzy system. This can
be either performed offline or online while the fuzzy
system is applied. Neural networks can only come
into play if the problem is expressed by a sufficient
amount of observed examples. On the one hand no
knowledge will be provided about the problem. On
the other hand, however, it is not straightforward to
extract comprehensible rules from the neural
network's structure.
Fig 18. Connection Time for each Addition of
Source
F. CONCLUSION
In this paper a novel control scheme is proposed for
regulating frequency and voltage stability of the
network through coordinated control of micro-grid by
Neuro-Fuzzy Algorithm. Sources for a micro-grid are
PV, Fuel Cell and Diesel Generator. Maximum
Power Point Tracking (MPPT) is done for PV
through P&O based Neuro-Fuzzy algorithm. PV
generator is controlled to operate at maximum
voltage during higher demand increase. Depending
on the difference between the demand and
The generation, the PV generator is controlled in
coordination
With DDG and FC outputs along with DRC to
deliver transient
as well as steady-state frequency regulation in a
DDG-PV-FC based Hybrid ac microgrid system
without any storage.
Fig. 15 Load Control for each Source
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International Journal of Engineering Trends and Technology (IJETT) – Volume 29 Number 2 - November 2015
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