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Modeling and Control of a Renewable Hybrid Energy System With Hydrogen Storage


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This paper deals with system integration and controller design for power management of a stand-alone renewable energy (RE) hybrid system, which is at the construction stage in Lambton College (Sarnia, ON, Canada). The system consists of five main components: photovoltaic arrays, wind turbine, electrolyzer, hydrogen storage tanks, and fuel cell. The model for each process component is developed, and all the components are integrated in a Matlab/Simulink environment. A two-level control system is implemented, comprising a supervisory controller, which ensures the power balance between intermittent RE generation, energy storage, and dynamic load demand, as well as local controllers for the photovoltaic, wind, electrolyzer, and fuel cell unit. Simulations are performed to document the efficacy of the proposed power management strategy.
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Modeling and Control of a Renewable Hybrid
Energy System With Hydrogen Storage
Milana Trifkovic, Mehdi Sheikhzadeh, Khaled Nigim, Senior Member, IEEE, and Prodromos Daoutidis
Abstract This paper deals with system integration and
controller design for power management of a stand-alone renew-
able energy (RE) hybrid system, which is at the construction stage
in Lambton College (Sarnia, ON, Canada). The system consists
of five main components: photovoltaic arrays, wind turbine,
electrolyzer, hydrogen storage tanks, and fuel cell. The model for
each process component is developed, and all the components
are integrated in a MATLAB/Simulink environment. A two-
level control system is implemented, comprising a supervisory
controller, which ensures the power balance between intermittent
RE generation, energy storage, and dynamic load demand, as well
as local controllers for the photovoltaic, wind, electrolyzer, and
fuel cell unit. Simulations are performed to document the efficacy
of the proposed power management strategy.
Index Terms—Hybrid system, hydrogen storage, model
predictive control (MPC), power management, renewable energy
RENEWABLE energy (RE) sources will become an
increasingly important part of power generation as the
reserves of fossil fuels get closer to depletion. Among avail-
able RE technologies, wind and solar energy sources are
the most promising options, as they are omnipresent, freely
available, and environmentally friendly. Although these tech-
nologies are improving in various aspects, the drawbacks
associated with them, such as their intermittent nature and
high capital cost, remain the main obstacles to their utilization.
Consequently, only 3% of total global electricity is generated
from nonhydro renewable sources [1].
Because of their intermittent nature, wind and solar energy
resources in a given area can be complementary on a daily
and/or seasonal basis. It has been shown that hybrid combina-
tions of two or more renewable power generation technologies
in stand-alone applications are economically viable and can
improve the system’s performance [2]–[6]. Additionally, in
order to ensure grid-like power for autonomous systems, a
storage medium or energy carrier is needed. The energy
Manuscript received May 2, 2012; revised November 19, 2012; accepted
January 20, 2013. Manuscript received in final form February 18, 2013. Date
of publication March 11, 2013; date of current version December 17, 2013.
Recommended by Associate Editor S. Varigonda.
M. Trifkovic and P. Daoutidis are with the Department of Chemical
Engineering and Materials Science, University of Minnesota, Minneapolis,
MN 550455 USA (e-mail:;
M. Sheikhzadeh and K. Nigim are with the Department of Instrumentation
and Control, Lambton College, Sarnia, ON N7S 6K4, Canada (e-mail:;
Color versions of one or more of the figures in this paper are available
online at
Digital Object Identifier 10.1109/TCST.2013.2248156
storage technologies can be classified into capacity-oriented
(pumped hydroelectric systems, compressed air, hydrogen) and
access-oriented storage devices (batteries, flywheels, superca-
pacitors, and superconducting magnetic energy storage) [7].
Each one of them has several advantages and disadvantages,
and one has to consider factors such as the operating cost,
power response time, efficiency and calendar life when select-
ing a suitable storage technology. For example, conventional
battery storage is energy efficient, but the cost of energy
storage is very high [8]. Pumped hydro is suitable for large-
scale applications but it is applicable only in certain locations.
Hydrogen is an attractive energy carrier since it is one of
the cleanest, lightest, and most efficient fuels, but it has a
slow power response time. The disadvantage of the slow
dynamics can be compensated by implementing a suitable
power management tool.
Proper sizing of each component in a hybrid energy system
is a key factor for its technoeconomic feasibility [9]–[11].
Unit sizing and technology selection can be based on meeting
requirements such as using the available generation technology
and not exceeding the equipment power rating, or on satisfying
constraints and achieving multiple objectives such as mini-
mizing environmental impact, installation and operating costs,
payback periods on investment, and maximizing reliability.
The optimization problem can sometimes have conflicting
objectives and thus be complex. A comprehensive survey
of studies that addressed the complexities involved in the
design of hybrid RE power generation technologies has been
reported [12].
Significant research effort has been devoted to the modeling
and control of individual process components as well as
integrated RE systems. Most of the studies that have dealt
with hybrid energy systems have been performed in the
simulation mode [5], [11], [13]–[17], with only a few dealing
with real-time application [18]–[21] due to the high capital
cost associated with design and implementation. The optimal
integration of hydrogen storage with RE sources and the power
management of such systems have also received considerable
attention [8]–[10], [18], [22]–[25].
The importance of a control strategy for the optimal oper-
ation of the photovoltaic (PV)/hydrogen/battery systems has
been shown previously [23]. The outputs from the various
generation sources of a hybrid energy system need to be
coordinated and controlled to realize its full benefit. Thus,
development of suitable power management that ensures meet-
ing the customer load demand despite the intermittent nature
of RE sources is an integral part of ensuring the system’s
1063-6536 © 2013 IEEE
reliability and achieving operational efficiency [26]. The aim
of this paper is to present a comprehensive study of the
automation system design for a stand-alone power system
located in Sarnia, ON, Canada. In a preliminary version of this
study, we introduced a simplified model and control strategy
for this system [27]. Here, we describe the comprehensive
model for the wind/PV/electrolyzer/fuel cell system and a
power management tool that utilizes decentralized adaptive
model predictive control (MPC) at the local control level and
decision-based control at the supervisory level. Specifically,
power generated from wind and PV is stored in the form
of hydrogen. Maximum power point tracking (MPPT) on
the PV system and the pitch angle and power controllers
on the wind turbine ensure optimal power generation by the
RE sources. The supervisory controller computes the power
references for the fuel cell and electrolyzer subsystems at
each sampling time. The power references are sent to the
local decentralized MPC system, which brings the fuel cell
and electrolyzer subsystems to the desired power reference
values while minimizing a suitable cost function. The perfor-
mance and effectiveness of the proposed control architecture
is evaluated through simulations.
Dynamic, first-principles models of the individual units
along with their system integration are described in the fol-
lowing subsections. It is assumed that the electrochemical
reactions in the electrolyzer and fuel cell are instantaneous.
Although the model does not include phenomena with a very
slow response (i.e., catalyst and membrane degradation in the
fuel cell and electrolyzer), it captures the essential dynamics
of the system and allows the implementation and evaluation
of the proposed control strategy.
A. Wind Energy Conversion System
The wind energy conversion system (WECS) consists of a
turbine to capture the energy in the wind, a drive train to speed
up the rotational speed of the shaft, and a generator to convert
the mechanical energy into electrical energy (see Fig. 1). In
this paper, a variable-speed wind turbine with the capability
of continuous adaptation (acceleration or deceleration) of the
rotational speed ωof the wind turbine to the wind speed vis
used. The main classification of variable-speed wind turbines
is according to their generator, among which WECS equipped
with doubly fed inductance generators (DFIGs) are the most
common type. The possibility of pitch control with an efficient
transmission of the power to the grid through active and
reactive power control has made them very attractive due to
the rising issue of the wind power impact on the electrical
network. In these types of generators, the stator of the machine
is directly connected to the grid and the rotor power is handled
by converters [28].
The WECS model consists of three main parts: wind tur-
bine rotor, drive train, and generator. The wind turbine rotor
converts the kinetic energy of the wind into mechanical energy
by producing torque. Since the energy contained in the wind
is in the form of kinetic energy, its magnitude depends on the
Fig. 1. Wind energy conversion block diagram.
air density and wind velocity. The wind power obtained by
the turbine rotor is given by [15], [25], and [29]
2ρAv3cp(λ, β ) (1)
where Pwis the power extracted from the wind, ρis the air
density, Ais the swept area by the wind, and cpis the power
coefficient which is a function of the tip speed ratio λand
the pitch angle of the rotor blades β. The tip speed ratio is
described as [29]
where ωmis the rotational speed and Ris the radius of the
wind turbine rotor.
The drive train transfers the power from the turbine rotor
to the generator. It includes the input rotating shaft connected
to the gear box and the output shaft connecting the drive train
to the generator. The main model equations for the drive train
are as follows [29], [30]:
dt =1
2HmTmKθmg Dmωm(3)
dt =1
2HgKθmg TeDgωg(4)
dt =ω0ωmωg(5)
where Tmis the accelerating torque, Teis the decelerating
torque, Kis the effective shaft stiffness, θmg is the twist in the
shaft system, ωgis the generator speed, ω0is speed constant of
the system, Dmωmis the damping torque in the wind turbine,
and Dgωgis the damping torque in the generator. It is assumed
that the shaft stiffness is constant.
A model that is commonly used for the induction generator
is the Park model [31]. The stator is directly connected
to the grid and the stator voltage (vs) is imposed by the
grid. The rotor voltage (vr) is controlled by a converter and
this voltage is used to control the captured power from the
wind generator [29]. A set of converters on the rotor side
provides an opportunity to manipulate the rotor side voltage
and consequently the captured power. The main generator
model equations are as follows [31]:
dt (6)
dt jωr
Fig. 2. Equivalent circuit model for a PV cell.
The stator and rotor fluxes are given by
isis the current space vector, vsand vrare the
rotor and stator voltage space vectors, respectively, Lmis the
magnetizing inductance, Lsand Lrare the rotor and inductor
self-inductances, respectively, Rsand Rrare the rotor and
stator resistance, respectively, and ψsand ψrare the rotor
and stator flux space vectors, respectively.
B. Solar Energy Conversion System
The solar energy conversion system or PV process is a
physical process through which solar energy is converted
directly into electrical energy. A solar cell is usually repre-
sented by an electrical equivalent one-diode model as shown in
Fig. 2.
The model contains a short-circuit current Isc , a diode, and a
series resistance RSand the resistance RPinside each cell and
in the connection between the cells. The correlation between
the output PV voltage and the current of a PV cell or a module
can be expressed as [32]
Ipv =Isc IDVD
Vpv =VDRSIpv (11)
where I0is the saturation current, VDis the diode voltage,
IDis the diode current, and VTis the diode voltage. Standard
PV characteristics are needed to solve the model, including
the short-circuit current Isc, the open-circuit voltage Voc,the
rated current IR, and the voltage VRat the maximum power
point (MPP) under standard test conditions (25 °C). The effect
of temperature on the PV panel is not considered. Cells are
normally grouped into modules, which are then connected in
arrays with MPparallel branches, each with MSmodules in
series. Under the assumption that the modules are identical
and are all exposed to the same ambient irradiation, the
solar cell arrays current and voltage (Iaand Va) can be
calculated as
Ia=MPIpv (13)
Fig. 3. Electrolyzer modeling block diagram.
C. Hydrogen Generation (PEM Electrolyzer) System
The electrolysis of water using cells with a polymer elec-
trolyte membrane (PEM) is a very efficient method of produc-
ing hydrogen. PEM electrolyzers are very simple and compact
and have demonstrated higher current density capability than
conventional alkaline water electrolyzers [33]. The reactions
that take place at the anode and the cathode of a PEM
electrolyzer are described below
Anode Reaction :H2O2H++1
Cathode Reaction :2H++2eH2.(16)
The supplied water to the anode side is decomposed into
oxygen gas, hydrogen protons, and electrons. The hydrogen
protons are transported through the proton conductive mem-
brane to the cathode side. At the same time, the electrons
exit the PEM electrolyzer cell via the external circuit, which
supplies the driving force (i.e., cell potential) for the reaction,
whereas at the cathode side the hydrogen protons and the
external circuit electrons recombine to form hydrogen gas.
The dynamic model for a PEM electrolyzer is composed of
four ancillaries: the anode, the cathode, the membrane, and
the voltage ancillary (Fig. 3). The anode ancillary calculates
oxygen and water flows and their partial pressures. The
cathode system calculates hydrogen and water partial pres-
sures and their flows. The membrane ancillary computes the
water content, electro-osmotic drag, water diffusion, and the
conductivity of the membrane. The voltage ancillary calculates
the electrolyzer’s voltage by incorporating the Nernst equation,
ohmic polarization, and activation polarization.
The material balance equations for the anode ancillary are
given by
Anode :dN
dt =Nin
dt =Nin
The number of moles of water NH2Oand oxygen NO2,the
electrolyzer temperature, and the water and oxygen partial
pressures pH2Oand pO2are used to calculate the anode total
pressure pAnode =pH2O+pO2and the oxygen mole fraction
yO2inside the anode channel using the ideal gas law and
thermodynamic properties [34]. Electrochemistry principles
are used to calculate the rates of oxygen generation Ngen
during the water splitting reaction. The rate of generated
oxygen is obtained from Faraday’s law as
O2=nele IeleηF
nst F(18)
where nele is the number of electrolyzer cells, Iele is the
electrolyzer applied current, nst is the reaction stoichiometry
coefficient, ηFis the Faraday efficiency, and Fis the Faraday
constant. The detailed calculations are given in [34].
Similar to the anode ancillary, the cathode molar flows of
water and hydrogen are obtained by calculating the partial
pressures pH2Oand pH2, respectively, cathode total pressure
pC, and the hydrogen mole fraction
Cathode :dN
dt =Nin
dt =Nin
The rate of hydrogen generated in the water-splitting reac-
tion, Ngen
H2, is a function of the stack current
H2=nele IeleηF
nst F.(20)
The water transport through the membrane is achieved by
electro-osmotic drag and diffusion phenomena [35], [36]. Note
that the membrane molar rate is needed to calculate the molar
rates in the anode and cathode ancillaries [see (17) and (19)].
The amount of water transported is dependent on the electro-
osmotic drag coefficient nd, which is defined as the number
of water molecules carried by each proton. Water diffusion
through the membrane is calculated by Fick’s law, and the
combination of these two phenomena is shown in the following
H2O=MH2OAele nd¯
where MH2Ois molecular weight of water, Ais the area
of the electrolyzer cell, ¯
Iele is the current density, Dwis
the water diffusion coefficient, cw,cand cw,aare the water
concentration at the cathode and anode surface, respectively,
and tmis the thickness of the membrane. The electro-osmotic
drag and diffusion coefficient vary with the water content in
the membrane, i.e., λm, and empirical relationships describing
these correlations are given in [36].
The total electrolyzer voltage can be represented as
Vele =Eele +Vact
ele +Vohm
ele (22)
where Eele is the open-circuit voltage, Vact
ele is the activation
polarization, and Vohm
ele is the ohmic polarization. The open-
circuit voltage (Eele), defined by the Nernst equation and the
activation and ohmic overpotentials are modeled according to
[34] and [37]
Eele =1
2FGele +RTele ln pele
ele =RTele
2βFln ¯
ele =¯
Iele Rohm
where Ris the universal gas constant, Gele is the Gibbs free
energy of formation, Tele is the absolute temperature, αele
is the water activity between the anode and the electrolyte,
Fig. 4. Fuel cell modeling block diagram.
and pele
H2and pele
O2are the partial pressures of hydrogen, and
oxygen, respectively. The activation polarization is a function
of the current density ¯
Iele, the exchange current density ¯
and the charge transfer coefficient β. The ohmic polarization
is a function of the membrane resistance Rohm
ele , which can
be calculated by using the membrane conductivity and its
thickness [34].
Assuming a lumped thermal capacitance model, the overall
thermal energy balance can be expressed as [22] and [25]
Cele dT
dt =˙
Qgen ˙
Qloss ˙
Qcool (24)
where Cele is the overall heat capacity of the electrolyzer, ˙
is the heat power generated inside the electrolyzer stack, ˙
is the heat power loss, and ˙
Qcool is the heat power loss due
to cooling. Each term in the thermal energy balance equation
is calculated as follows:
Qgen =nele Iele (Vele Vth)
Qloss =TeleT0
Qcool =Cm
ele Tout
where Vth is the thermal voltage, T0is the ambient tem-
perature, Rth
ele is the thermal resistance, Cm
ele is the cooling
medium overall heat capacity, and Tmis the cooling medium
D. Hydrogen Consumption (Fuel Cell) System
The reverse equivalent of a PEM electrolyzer is a PEM fuel
cell, which is thus modeled similar to the PEM electrolyzer
described in the previous section. Chemical energy of the
hydrogen fuel is converted into electricity through a chemical
reaction with oxygen. The byproducts of this reaction are
water and heat. The dynamic fuel cell model used here was
developed in [38] and it is divided into four main ancillaries:
the anode, the cathode, the membrane, and the voltage (Fig. 4).
The mole balance equations for oxygen, nitrogen, hydrogen,
and water mass on the anode and cathode side of the PEM
fuel cell can be written as follows:
Anode :dN
dt =Nin
dt =Nin
Cathode :
dt =Nin
dt =Nin
dt =Nin
The molar rate of water inside the cathode, NH2O, depends
on the summation of vapor flows, because it is assumed that
the liquid water does not leave the stack and evaporates into
the cathode gas if cathode humidity drops below 100%. Water
is in vapor form until the relative humidity of the gas exceeds
saturation (100%), at which point the vapor condenses into
liquid water [39]. Similar to the electrolyzer, the ideal gas law,
thermodynamic properties, and electrochemistry principles can
be used to calculate the components’ partial pressures, total
pressure at the anode and cathode, moles of reacted hydrogen
and oxygen, as well as the generated water [38].
The fuel cell voltage is calculated based on voltage drops
associated with all the losses as follows:
Vfc =Efc Vact
fc Vohm
fc Vconc
fc (28)
where Vfc is the fuel cell voltage, Efc is the open-circuit
voltage, Vact
fc is the activation polarization, Vohm
fc is the ohmic
polarization, and Vconc
fc is the concentration overpotential. The
open-circuit voltage and ohmic polarization are calculated as
in 23. The activation and concentration overpotentials are
obtained by the following equations:
fc =Vact
fc =¯
Ifc cconc,1¯
fc cconc,2(29)
where ¯
Ifc is the fuel cell current density, Vact
0is the voltage
drop at zero current density, and a cact and cconc and ¯
fc are
constants that depend on the temperature and reactant partial
pressure and are obtained empirically [38].
The heat generated by the fuel cell chemical reaction can
be written as
Cfc dT
dt =˙
Qgen ˙
Qelec ˙
Qloss (30)
where ˙
Qgen is the heat generated from chemical reaction, ˙
is the generated electrical energy, ˙ the absorbed latent
and sensible heat, and ˙
Qloss is the heat loss. These terms are
given by the following relations:
Qgen =Nreac
Qelec =Vfc Ifc
iTfc Nin
Qloss =hfc Afc (Tfc Tamb)
where Hreac
fc is the enthalpy of reaction, Ciis the specific
heat capacity, Hvis the heat of evaporation, hfc is the fuel
cell heat transfer coefficient, Afc is the fuel cell surface area,
and Tamb is the ambient temperature. The detailed model
for all the fuel cell ancillaries can be found in [25], [38],
and [40].
E. Hydrogen Storage System
Hydrogen storage consists of a compressor and a hydrogen
tank. The required compression work can be estimated as
follows [25]:
Pcomp =Nout
H2|ele 2ncRTin
PinPout nc1
where Pcomp is the compressors consumed power, Tin is
the hydrogen temperature from the electolyzer (assumed to
be equal to Tele), and ηcis the compressor efficiency. The
hydrogen mole balance in the tank is obtained as
dt tank =Nin
dt fc Nout
dt ele
Accumulated hydrogen in the tank calculated by 33 is used
to estimate the hydrogen pressure in the tank under the
assumption that the tank temperature Ttank is constant, using
the Beattie–Bridgeman equation [25]
Ptank =Ntank
tank 1a1Ntank
Vtank 
Vtank Ntank
where Ptank is the tank pressure, Vtank is the tank volume, and
a1a5are empirical parameters [25].
A multilevel control scheme has been reported as a more
practical and efficient hierarchy for controlling hybrid energy
systems [41]. The applied control structure for the system
studied here consists of two layers: the supervisory controller
and low-level local controllers. The supervisory control layer
monitors and controls the power flow from the RE sources to
the storage components and power consumption centers. It also
computes the operating trajectories for the fuel cell and elec-
trolyzer subsystems. The local controller layer is responsible
for regulating each process component to improve efficiency
and optimize its performance. All process subsystems and their
controllers are connected to the supervisory controller.
The applied control scheme aims to fulfill the following
1) Optimally using the energy resources.
2) Meeting the load demand.
3) Operating the system efficiently.
In the following subsections, these two control layers are
described in detail.
A. Supervisory Power Control
The hybrid energy system consists of the power generation
(wind, PV, and fuel cell) and the power consumption com-
ponents (electrolyzer, auxillary equipment, and the main load
demand). Power flow in the hybrid system is shown in Fig. 5.
The net power (Pnet), which is the difference between the
generation sources and the load demand, is calculated as
Pnet =Pwind +Ppv(Pload +Pae)(35)
where Pwind and Ppv are the power generated by the wind
and solar energy conversion systems, respectively, Pload is the
load demand, and Pae is the power consumed by auxiliary
equipment in the system.
The generated power from the renewable sources can be
either used directly to meet the load demand or transferred to
the hydrogen production process. Because of the intermittent
nature of RE as well as the dynamic load demand, Pnet can
have a positive, zero, or negative value at any instant. In
the case of Pnet =0, there is sufficient power generated
from the renewable sources to meet the load and auxiliary
equipment demand with neither excess nor deficit of energy.
The electrolyzer and fuel cell activation and deactivation are
basedonthePnet value which is calculated in each sampling
interval. When there is excess power generated (Pnet >0), the
electrolyzer is activated. On the other hand, when there is a
deficit in power generation (Pnet <0), the fuel cell stack is
activated to consume previously stored hydrogen and convert it
to electricity. The fuel cell activation will occur only if there is
a sufficient supply of hydrogen in the storage tank. Otherwise,
the hybrid system enters a “hydrogen starvation” mode. This
can occur as a consequence of either extreme operational
conditions, such as low availability of renewable sources and
very high load demand, or inappropriate unit sizing. The power
management logic is shown below
If (Pnet >0)
ζele =1
fc =0
comp =1
If Pnet >0&Ptank Pub
ζele =0
fc =0
comp =0
If Pnet <0&Ptank Plb
ζele =0
fc =1
comp =0
If Pnet <0&Ptank <Plb
ζele =0
fc =0
comp =0
If (Pnet =0)
ζele =0
fc =0
comp =0.
In the above, Plb
tank and Pub
tank are the low hydrogen pressure
tanks lower and higher limits, respectively, and ζele,ζfc,and
ζcomp are the operational modes (ON/OFF) for the electrolyzer,
fuel cell, and compressor, respectively. According to (36), the
electrolyzer becomes activated as soon as there is positive Pnet.
However, if the excess power is less than the electrolyzers
rated power, the generated power will be completely used to
keep the electrolyzer running while not satisfying the load
demand. Equation (36) can be modified as follows to prevent
Fig. 5. Supervisory power management block diagram.
this problem
If Pnet >Prated
ζele =1
fc =0
comp =1(37)
where Prated
ele is the electrolyzers rated power.
B. Local Controllers
Each component in the studied hybrid energy system has
its own local controller which enforces optimal operation of
the corresponding unit based on the available information with
respect to power generated from the WECS and PV, and power
1) Wind System Controllers: The wind turbine power output
varies with the wind speed, and this dependency is represented
by a wind turbine characteristic curve. The characteristic curve
has three distinct zones according to the corresponding wind
speeds: cut-in, rated, and cut-out. Below the cut-in wind speed
and above the cut-out wind speed, the output power is zero.
When the wind velocity is between the cut-in and rated
wind speeds, the local controller is responsible to extract the
maximum power according to the wind turbine characteristic
curve. This is achieved by controlling the active and reactive
power of the rotor. The control scheme consists of two series
of two proportional integral (PI) controllers. The actual turbine
speed m)and wind turbine characteristic are used to estimate
the maximum possible power as a reference. The active and
reactive power is compared to its reference, and the offset
for both are sent to two stage controllers to adjust the current
and voltage of the rotor converter side in order to obtain the
maximum possible power. The switching dynamics, the power
losses in the converter, and delays caused by the intermediate
dc converter are assumed to be negligible [42], [43].
Between the rated and cut-out wind speeds, the DFIG
wind turbine activates a blade pitch angle controller to reduce
the power coefficient and, consequently, the extracted power
from the wind. This controller prevents high generator speed,
and hence prevents mechanical damage of the turbine. The
control action is based on comparing the generator speed to
its reference and sending the error signal to PID controller,
which estimates the reference value for the pitch angle.
The offset between reference and actual pitch angle is mini-
mized by a second P controller.
2) Solar System Controllers: Despite all improvements, PV
modules still have a relatively low conversion efficiency. The
voltage–current–Power (VIP) characteristic curves for a
PV array specifies a unique operating point at which the
maximum possible power is delivered. The MPPT algorithm
is used for extracting the maximum available power from the
PV module under certain voltage and current conditions. There
are several MPPT techniques reported in the literature [44],
[45]. The perturbation and observation method (P&O) is one
of the most common and effective ways of power tracking
for PV arrays [45]. In this paper, the current perturbation
and observation method (CP&O) is applied [45]. The MPP
tracker operates by periodically incrementing or decrementing
the solar array current (Ipv). If a given perturbation leads to an
increase (decrease) of the output power of the PV (Ppv), then
the subsequent perturbation is generated in the same (opposite)
direction. The perturbation magnitude was set to 0.02 A.
3) Model Predictive Control of Electrolyzer and Fuel Cell:
Implementation of power control over the electrolyzer and
the fuel cell can improve their efficiency and consequently
the hydrogen generation and storage. The constraints and
dynamics of the electrolyzer and fuel cell are decoupled as
they operate in a sequential mode; i.e., when the fuel cell is
ON (OFF), the electrolyzer is OFF (ON). A decentralized MPC
scheme was employed to regulate the power of the electrolyzer
and fuel cell. A key advantage of MPC is its ability to deal
with constraints in a systematic and straightforward manner.
This is of particular importance for the PEM electrolyzer
and fuel cell operation, where abrupt changes in the current
load produce more uneven water/current distribution and
promote degradation of the membrane, which in turn
decreases the overall efficiency and the working life of these
units. A decentralized approach is the most appropriate one
because of the limited exchange of information between the
subsystems [46]. Moreover, a decentralized implementation of
MPC has the advantage of reducing the optimization problem
into a number of smaller and easily tractable ones. Each
controller determines the constraint-admissible and optimum
value of the current that can be applied on the electrolyzer/fuel
cell at each sampling time. For control design purposes,
the nonlinear models of the electrolyzer and fuel cell were
linearized and discretized using the first-order hold conversion
method. The resulting state space model has the form
where kis the sampling time, and A,B,D,andCare matrices
of appropriate dimensions. x,u,d,andyare the model
states, manipulated variables, disturbances, and model outputs,
respectively. The electrolyzer state space model variables are
xele =δNa
yele =δPeleVeleNH2pH2T
uele =[δIele],
dele =δTa
where the operator δindicates the deviation from the
operating point, and the aand csuperscripts stand for anode
and cathode, respectively. Pele is the controlled variable,
while the rest of the outputs are measured ones.
The fuel cell state space model variables are
xfc =δpfcNa
yfc =[δPfcVfc]T
ufc =[δIfc]
dfc =δTc
where Pfc is the fuel cell generated power chosen as the
controlled variable, Tc
fc is the input air temperature, and Vfc is
the fuel cell voltage (the measured output). For both systems,
the control objective is imposed at any instant by the Pnet
value from the power management controller [(35)–(37)].
The model predictive controller is designed to mini-
mize the following finite horizon control and performance
uJ(x(t), u(t), t)=
k=1Wy[y(k)y(k)ref ]2
k=1Wu[u(k)u(k)ref ]2
subject to :
y(k)lb <y(k)<y(k)up
u(k)lb <u(k)<u(k)up
u(k)lb <u(k)<u(k)up
where Wyand Wuare input and output weight factors for
each variable, and HPand HCare the prediction and control
horizons, respectively. The objective function was subjected to
a set of constraints, consisting of the fuel cell and electrolyzer
output power upper and lower limits (yub,ylb), current upper
and lower limits (uub,ulb), and the rate of change in the
electrolyzer and fuel cell current (u). The output power
upper and lower limits (yub,ylb)are defined by the fuel cell
and electrolyzer power operating range.
Two sets of operating conditions, which correspond to 10%
and 90% of the rated power for the fuel cell and electrolyzer,
were used for model linearization. The resulting linear mod-
els are provided in Supporting Information. Based on these
linearized models, two MPC controllers were designed for
each unit. In each control period, a suitable controller is
chosen to enforce the remote power set point calculated by the
supervisory controller. If the signal value was <50% of the
rated power, the low operating range controller was selected.
Otherwise, the high operating range controller was chosen.
This strategy is advantageous, as it minimizes the model
mismatch by having multiple models while significantly sim-
plifying its implementation, which is of particular importance
for real-time studies.
Aside from the power control, the fuel cell has two addi-
tional PI controllers which minimize the pressure difference
between the cathode and anode by manipulating the hydrogen
flow and keep the desired air humidity by injecting the
appropriate amount of water vapor into the air stream entering
the cathode side, respectively.
Wind system
Rated power 4kW
Cut-in speed 3m/s
Cut-out speed 12 m/s
Rated speed 7m/s
PV system
Rated power 120 W/panel
Total rated power 2.4 kW
PV array 20 ×2
Temperature 25 °C
Rated power 2.8 kW
H2Flow rate 8slpm
Output pressure 14 atm
Tank vo lum e 50 m3
Tank pressure 150–200 atm
Compressor pressure 200 atm
Fuel cell Rated power 1.9 kW
H2Flow rate 15 slpm
Load Power 1–2 kW
The proposed hybrid stand-alone system model consisting
of the previously described components was implemented
using the MATLAB and Simulink software. Model testing
was performed under various conditions using historical wind
data, irradiance, ambient temperature, as well as dynamic load
demand data. The sizing of the process components in the
existing stand-alone system is shown in Table I.
The sizing of the various process components was per-
formed according to the electricity energy balance for small
loads typical of residential demand. This load demand is
intermittent in nature, and it was assumed that its minimum,
maximum, and average alues are 0.5, 1.9, and 1 kW, respec-
tively. The sizing was performed according to the net power,
which is estimated based on the difference of the power
generated by wind and PV and the power demand at all times.
Assuming a capacity factor (combination of the overall unit
efficiency and the effect of geographical location) of 15% and
10% for the wind turbine and PV array, respectively, 4-kW
ratedwindpowerand2.4-kWPVrated power were estimated.
The fuel cell needs to supply the maximum load demand when
there is no sufficient power generated by the PV and wind.
Therefore, the estimated size of the fuel cell stack is 1.9 kW.
The electrolyzer capacity should be adequate to use the surplus
power from the RE sources. The maximum excess power will
occur when there is minimum load and maximum power from
the RE sources, which corresponds to 6.4 kW of electrolyzer
capacity. However, the situation when both wind and solar
power reach their maximum points while the load demand is
at its lowest is very unlikely, and thus a 2.8 kW capacity for
the electrolyzer was used.
The presented simulation results are based on the average
weather data for a winter day in the Sarnia, Ontario region, and
the load demand for the installed stand-alone system. Table II
presents the PID tuning parameters for the various low-level
Fig. 6. RE conversion systems. (a) Wind speed and solar data. (b) Generated
power. (c) Pitch angle controller output for wind turbine.
controllers applied on the wind and fuel cell subsystems. In
this table, kc,τi,andτdare the proportional, integral, and
derivative constants, respectively.
The wind and irradiance data along with the corresponding
generated power is shown in Fig. 6(a) and (b). As was
mentioned previously, between the cut-in and rated speeds
(3 and 7 m/s) the WECS power control is regulating the
generator converter to generate the maximum possible power
by manipulating the turbine speed. Above the wind speed of 7
m/s, the pitch controller is maintaining the power at the rated
wind turbine power, as is shown in 6(c).
Fig. 7(a) shows the total generated power, the load
demand, and their difference (Pnet). The Pnet trend shown in
Fig. 7(a) is used to activate or deactivate the hydrogen system
components. Fig. 7(b) presents the electrolyzer and fuel cell
status throughout the simulation period. When Pnet >Pele,
Wind pitch controller Reference pitch estimator PD controller: kc=35
Pitch angle controller P controller: kc=500
Wind power controller
Rotor side current controller PI controller: kc=0.3
Grid side current controller PI controller: kc=1
Voltage controller PI controller: kc=0.02
Fuel cell controller Hydrogen flow controller PI controller: kc=1.2
Air humidity controller P controller: kc=1
Fig. 7. Power balance and hydrogen system components activation. (a) Power
trends including net power, total generated RE and load demand with auxiliary
equipment consumption. (b) Electrolyzer and fuel cell activation/deactivation.
there is excess power available for hydrogen generation, which
will result in the activation of the electrolyzer at its rated
capacity. In the case of Pnet <0, the fuel cell is activated
to supply the power deficit [see Fig. 7(b)].
As mentioned previously, the objective of the power man-
agement supervisory controller is not only to enable and
disable the hydrogen system components but also to send the
remote set point to the fuel cell and electrolyzer via Pnet.The
model predictive controller was designed for the electrolyzer
and fuel cell and then integrated with the nonlinear model of
the plant. The length of the prediction horizon affects both the
computational time and the performance of the system. The
prediction horizon (Wy) and control horizon (Wu)wassetto
15 and 8 intervals for the electrolyzer, and 10 and 4 intervals
for the fuel cell. The operational range for the electrolyzer and
fuel cell is 200–2800 and 100–1900 W, respectively. A variable
Fig. 8. Electrolyzer MPC. (a) Performance in terms of tracking net power.
(b) Generated hydrogen.
Fig. 9. Fuel cell MPC. (a) Performance in terms of power generated.
(b) Consumed hydrogen.
sampling time with maximum size of 1 s was used for data
measurement. The remote set point for the MPC controllers
was Pnet for the electrolyzer and |Pnet|for the fuel cell.
Figs. 8 and 9 show the performance of the MPC controllers
implemented for the electrolyzer and fuel cell, respectively.
The controllers show robust set-point tracking despite the
variation in the set points. It is important to note that the
hydrogen generation by the electrolyzer and its consumption
by the fuel cell are significantly more efficient. Also, note that
the ability to run the electrolyzer at lower capacity enables
its activation below its rated power (Prated
ele ). This in turn
results in using the RE more efficiently and, consequently,
in higher hydrogen generation. More importantly, MPC elim-
inates frequent turning on and off of the electrolyzer, which
can decrease the lifespan of the unit drastically. For the fuel
cell, we demonstrate two criteria for the fuel cell activation
without the power controller (Fig. 9). In the first criterion
(without MPC1), Pnet <0and|Pnet|>Pfc, which results in
the more conservativehydrogen usage but also fails to meet the
load demand. On the other hand, the second criterion (without
MPC2), Pnet <0and|Pnet|<Pfc, resulting in overgeneration
of electricity as the fuel cell is always operated at its rated
capacity with previously stored hydrogen depleting rapidly.
The implementation of the MPC eliminates these problems
and results in the successful demand tracking and adequate
hydrogen usage, as shown in Fig. 9.
A comprehensive detailed model for a stand-alone hybrid
energy system with wind turbine and solar energy conversion,
electrolyzer, fuel cell, and hydrogen storage components was
developed. A supervisory controller for proper power man-
agement and a set of local controllers for efficient hydrogen
generation and consumption were implemented. A model
predictive controller was designed for optimal operation of the
electrolyzer and fuel cell. The controller performance showed
significant improvement in the utilization of both components,
and consequently better power management of the hybrid
energy system could be achieved in comparison to the case
when there was no model predictive controller. Future work
will focus on the model validation and implementation of the
power management tool in real time on the hybrid system built
in Lambton College. We also plan to implement a dynamic
optimization formulation at the supervisory level which would
account for the weather and demand prediction to ensure
smooth operation and minimization of the operational cost.
The authors would like to thank F. Hernandez for his help
with data collection and S. Karimi for useful discussions.
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Milana Trifkovic received the B.S.E. and Ph.D.
degrees in chemical engineering from the University
of Western Ontario, London, ON, Canada, in 2006
and 2010, respectively.
She is a Post-Doctoral Fellow with the Department
of Chemical Engineering and Materials Science,
University of Minnesota, Minneapolis, MN, USA.
Her current research interests include polymer mate-
rial processing, RT control systems, microgrids, and
distributed generation.
Mehdi Sheikhzadeh received the B.S.E. degree
from Ferdowsi University, Mashhad, Iran, the M.S.E.
degree from the Sharif University of Technology,
Tehran, Iran, in 1997 and 1999, respectively, and
the Ph.D. degree from the University of Western
Ontario, London, ON, Canada, all in chemical engi-
He is currently an Industrial Research Chair at
Colleges granted by the Natural Sciences and Engi-
neering Research Council of Canada and is a Profes-
sor of the Instrumentation and Control Program with
Lambton College. His current research interests include modeling, advanced
process control and optimization of energy systems.
Khaled Nigim (M’85–SM’00) received the Ph.D.
degree in electrical engineering from the University
of Leicester, Leicester, U.K., in 1983.
He is currently the Research Lead in develop-
ing power management controller in the renew-
able energy conversion and storage research project
funded by NSERC, and is a registered Professional
Engineer in Ontario, Canada. His current research
interests include renewable energy resources integra-
tion, islanding strategy, challenging and opportuni-
ties of distributed generation fuelled by alternative
energy sources, the development of AC and DC micro-grid concepts as well as
reactive power compensation for wind farms and photovoltaic energy parks.
Prodromos Daoutidis received the Diploma degree
in chemical engineering from the Aristotle Univer-
sity of Thessaloniki, Thessaloniki, Greece, and the
M.S.E. degrees in chemical engineering and elec-
trical engineering systems and the Ph.D. degree in
chemical engineering from the University of Michi-
gan, Ann Arbor, MI, USA, in 1987, 1988, and 1991,
He is a Professor with the Department of Chemical
Engineering and Materials Science, University of
Minnesota, Minneapolis, MN, USA. His current
research interests include control of nonlinear and distributed parameter
systems, control of differential algebraic systems, model reduction, chemical
and biological reaction systems, control of advanced materials processing, and
design and control of energy systems.
... Such systems are analyzed at various levels and in several component constellations. Many modelling studies deal with dynamic real-time operation and control, examining relatively small time scales of a few hours, a day, or up to two weeks, such as [7][8][9][10]. Certain component constellations have been examined quite often, such as hybrid energy systems designed for wind power as the only energy source [11]; others assume hydrogen as the only kind of energy storage [12]. ...
... However, it did not consider an FC and real load profiles. In [8], a hydrogen-based energy system model created in Simulink was presented. The authors used a time horizon of 24 h with a focus on power management and control systems. ...
This paper presents a model of an energy system for a private household extended by a lifetime prognosis. The energy system was designed for fully covering the year-round energy demand of a private household on the basis of electricity generated by a photovoltaic (PV) system, using a hybrid energy storage system consisting of a hydrogen unit and a lithium-ion battery. Hydrogen is produced with a Proton Exchange Membrane (PEM) electrolyser by PV surplus during the summer months and then stored in a hydrogen tank. Mainly during winter, in terms of lack of PV energy, the hydrogen is converted back into electricity and heat by a fuel cell. The model was created in Matlab/Simulink and is based on real input data. Heat demand was also taken into account and is covered by a heat pump. The simulation period is a full year to account for the seasonality of energy production and demand. Due to high initial costs, the longevity of such an energy system is of vital interest. Therefore, this model was extended by a lifetime prediction in order to optimize the dimensioning with the aim of lifetime extension of a hydrogen-based energy system. Lifetime influencing factors were identified on the basis of a literature review and were integrated in the model. An extensive parameter study was performed to evaluate different dimensionings regarding the energy balance and the lifetime of the three components, electrolyser, fuel cell and lithium-ion battery. The results demonstrate the benefits of a holistic modelling approach and enable a design optimization regarding the use of resources, lifetime and self-sufficiency of the system.
... PV-wind-battery [106] PV-wind-hydrogen fuel cell [107], [108] PV-ESS [109], [110] FCS-MPC is finite control set MPC, CCS-MPC is continuous control set MPC, DO-MPC is discrete observer MC, MPPT is maximum power point tracking, PMSG is permanent magnet synchrous generator, and PV is photovoltaic. ...
... The afore-discussed DERs can also be combined in hybrid format. For instance, MPC can be applied to provide optimal references to control hybrid combinations of PV-wind-battery [106], PV-wind-hydrogen fuel cell [107], and PV-ESS [109]. ...
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As the smart grid evolves, it requires increasing distributed intelligence, optimization and control. Model predictive control (MPC) facilitates these functionalities for smart grid applications, namely: microgrids, smart buildings, ancillary services, industrial drives, electric vehicle charging, and distributed generation. Among these, this article focuses on providing a comprehensive review of the applications of MPC to the power electronic interfaces of distributed energy resources (DERs) for grid integration. In particular, the predictive control of power converters for wind energy conversion systems, solar photovoltaics, fuel cells and energy storage systems are covered in detail. The predictive control methods for grid-connected converters, artificial intelligence-based predictive control, open issues and future trends are also reviewed. The study highlights the potential of MPC to facilitate the high-performance, optimal power extraction and control of diverse sustainable grid-connected DERs. Furthermore, the study brings detailed structure to the artificial intelligence techniques that are beneficial to enhance performance, ease deployment and reduce computational burden of predictive control for power converters.
... By contrast, in EES mode, the FC supplies power when renewable power is low and load demand is high. In general, the electrolyzer and FC operate independently to prevent a decrease in their lifespans and operational safety, thus enabling the HES to operate in HGS and EES modes both simultaneously and separately [12,13]. generation and load demand under normal operating conditions. ...
... By contrast, in EES mode, the FC supplies power when renewable power is low and load demand is high. In general, the electrolyzer and FC operate independently to prevent a decrease in their lifespans and operational safety, thus enabling the HES to operate in HGS and EES modes both simultaneously and separately [12,13]. Given the structural and operational fundamentals, an HES system is modeled with state variables (i.e., X H = [T E , V Fa , T F , H F , O F , H T , m mh , T T ] T ) that significantly affect its dynamic response, as discussed in Sections 2.2 and 2.3. ...
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Hydrogen energy storage (HES) systems have recently received attention due to their potential to support real-time power balancing in a power grid. This paper proposes a data-driven model predictive control (MPC) strategy for HES systems in coordination with distributed generators (DGs) in an islanded microgrid (MG). In the proposed strategy, a data-driven model of the HES system is developed to reflect interactive operations of an electrolyzer, hydrogen tank, and fuel cell, and hence the optimal power sharing with DGs is achieved to support real-time grid frequency regulation (FR). The MG-level controller cooperates with a device-level controller of the HES system that overrides the FR support based on the level of hydrogen. Small-signal analysis is used to evaluate the contribution of FR support. Simulation case studies are also carried out to verify the accuracy of the data-driven model and the proposed strategy is effective for improving the real-time MG frequency regulation compared with the conventional PI-based strategy.
... Zhao et al. [118] adjusted the reactive power output of a wind farm and the network infrastructure at the same time to reduce system actual power losses and bus voltage deviations, resulting in enhanced power control and voltage profile. Trifkovic et al. [119] used decentralized adaptive model prediction control and decision-making techniques to describe a power management strategy for a wind-PV-electrolyzer-fuel cell integrated standalone system. It was discovered that operating the electrolyzer at a lower power level increased the efficiency of the renewable energy produced, resulting in more hydrogen production. ...
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Smart microgrids, as the foundations of the future smart grid, combine distinct Internet of Things (IoT) designs and technologies for applications that are designed to create, regulate, monitor, and protect the microgrid (MG), particularly as the IoT develops and evolves on a daily basis. A smart MG is a small grid that may operate individually or in tandem with the electric grid, and it is ideal for institutional, commercial, and industrial consumers, as well as urban and rural societies. A MG can operate in two methods (stand-alone and grid-connected), with the ability to transition between modes due to local grid faults, planned maintenance, expansions, deficits and failures in the host system, and other factors. Energy storage is the process of storing and converting energy that can be used for a variety of purposes, including voltage and frequency management, power backup, and cost optimization. IoT is designed to deliver solutions for optimal energy management, security protocols, control methods, and applications in the MG, with numerous distributed energy resources (DER) and interconnected loads. The use of IoT architecture for MG operations and controls is discussed in this research. With the use of power grid equipment and IoT-enabled technology, MGs are enabling local networks to give additional services on top of the essential supply of electricity to local networks that operate simultaneously or independently from the regional grid. Additionally, this review shows how hybrid AC/DC MGs are advantageous compared to AC and DC MGs. The state-of-the-art optimization techniques and trends in hybrid MG research are included in this work.
Purpose The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources. Design/methodology/approach The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system. Findings The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation. Research limitations/implications It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress. Practical implications The practical hardware implementation is under progress. Social implications If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process. Originality/value If this system is implemented in real-time environment, every farmer gets benefitted.
Power-to-X technologies are a promising means to achieve Denmark's carbon emission reduction targets. Water electrolysis can potentially generate carbon-neutral fuels if powered with renewable electricity. However, the high variability of renewable sources threatens the Power-to-X plant's cost-efficiency, instead favouring high and constant operation rates. Therefore, a diversified electricity supply is often an option to maximise the load factor of the Power-to-X systems. This paper analyses the impact of using different power sources on the cost of production and the carbon intensity of hydrogen produced by a Power-to-X system. GreenLab Skive, the world's first industrial facility with Power-to-X integrated into an industrial symbiosis network, has been used as a case study. Results show that the wind/PV/grid-connected electrolyser for hydrogen and electricity production can reduce operational costs and emissions, saving 30.6 × 10⁷ kgCO2 and having a Net Present Value twice higher than a grid-connected electrolyser. In addition, the carbon emission coefficient for this configuration is 3.5 × 10⁻² kgH2/kgCO2 against 7.0 gH2/gCO2 produced by Steam Methane Reforming. A sensitivity analysis detects the optimal capacity ratio between the renewables and the electrolyser. A plateau is reached for carbon emission performances, suggesting a wind/grid-connected electrolyser setup with a wind farm three times the size of the electrolyser. Results demonstrate that hydrogen cost is not competitive yet with the electricity, suggesting an investment cost reduction but can be competitive with the current hydrogen price if the wind capacity is less than three times the electrolyser capacity.
In this paper, an integrated of buck and half‐bridge high step‐down converter utilizing single‐stage driving design for high‐efficiency energy conversion is proposed. The proposed converter has the ability to instantly and synchronously transfer energy from input to output within one conversion period. The proposed topology is capable of lowering the primary side voltage of the transformer, so the turns ratio could be reduced. The coupling rate is therefore better along with the leakage inductance. Furthermore, the duty cycle operated in an extreme situation is not necessary. As a combination of the advantages of high step‐down conversion, lower voltage stresses, and fewer amount of semiconductor elements, the feasibility of the proposed topology is verified by the implementation of a 300 W prototype. The proposed integrated topology utilizes the single‐stage energy transfer control algorithm to verify that the proposed experimental circuit has a full load efficiency of 92.4%, in the case of input voltage 380 V and output voltage 5 V, and the maximum efficiency, occurring at 100 W, is 94.55%. In this paper, an integrated of buck and half‐bridge high step‐down converter utilizing single stage driving design for high efficiency energy conversion is proposed. The proposed converter has the ability to instantly and synchronously transfer energy from input to output within one conversion period. As a combination of the advantages of high step‐down conversion, lower voltage stresses, the feasibility of proposed topology is verified. The full‐load efficiency of 92.4%, in the case of input voltage 380 V and output voltage 5 V.
In the past decade, producing chemicals from renewable energy for use as fuel has gained considerable interest. Renewable hydrogen production (PtH) is the backbone of this power-to-x concept, while further conversion to methanol (PtM) or ammonia (PtA) serves to increase energy density. In this article, we review production and utilization technologies for PtH, PtM, and PtA in the context of the energy and transportation sectors. Specifically, each technology’s basic operating principals, state of development, energy efficiency, dynamic flexibility, and deployment outlook is discussed. We also review recent process systems engineering research of PtH, PtM, and PtA. At the process level, this research largely aims to improve economics through optimal synthesis and design of novel processes as well as coupled real-time operation and control for dynamic operation. At the facility or supply chain level, combined capacity planning and scheduling to optimally use intermittent renewable resources is the major focus.
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A numerical model is developed to predict the mass flow between channels in a Polymer Electrolyte Membrane (PEM) fuel cell with a serpentine flow path. The complete three-dimensional Navier–Stokes equations with multi-species mixture are solved and electro-chemical reactions are modeled as mass source/sink terms in the control volumes. The results indicate that flow distribution in both anode and cathode channels are significantly affected by the mass consumption patterns on the Membrane Electrode Assembly (MEA). The water transport is governed by both electro-osmosis and diffusion processes. Further, the overall pressure drop is less than that expected for a regular straight channel flow.
Energy is the basis of any technical and industrial development. As long as only human and animal labour is available, a main prerequisite for social progress and general welfare is lacking. The energy consumption per capita in a country is thus an indicator of its state of technical development, exhibiting differences of more than two orders of magnitude between highly industrialised and not yet developed countries.
Conference Paper
This study deals with system integration and controller design for power management of a stand-alone renewable energy (RE) hybrid system, which is in its construction stage in Lambton College (Sarnia, Ontario, Canada). The system consists of five main components: photovoltaic (PV) arrays, wind turbine, electrolyzer, hydrogen storage tanks, and fuel cell. The model for each process component is developed and all the hybrid energy system components are integrated in a MATLAB/Simulink environment. A hierarchical control system is implemented, comprising of a supervisory controller, which ensures the power balance between intermittent renewable energy generation, energy storage and dynamic load demand, and local controllers for the PV, wind, electrolyzer and fuel cell units. A decentralized model predictive control (MPC) was designed and implemented on the electrolyzer and fuel cell to ensure optimal power flow.
This paper presents an overview of the current process systems opportunities in power generation, storage and distribution. It puts in perspective how process systems engineering (PSE) has contributed to the area and explores the current technical problems that PSE can contribute to. Fuel cells, solar cells, wind turbines, flow batteries and rechargeable batteries as well as their interactions with the smart grid are considered. PSE has contributed and will contribute to the design as well as optimal integration and operation of power generators, storage systems and power grids, through mathematical modeling, control and optimization.
In this work, we propose a conceptual distributed control framework for electrical grid integrated with distributed renewable energy generation systems in order to enable the development of the so-called “smart electrical grid”. First, we introduce the key elements and their interactions in the proposed control architecture and discuss the design of the distributed control systems which are able to coordinate their actions to account for optimization considerations on the system operation. Subsequently, we focus on a specific wind/solar energy generation system connected to a reverse osmosis water desalination system and the electrical grid and design two supervisory predictive controllers via model predictive control to operate the integrated system taking into account short-term and long-term optimal maintenance and operation considerations, respectively. Simulations are carried out to illustrate the applicability and effectiveness of the proposed approach.
The design of the automation system and the implemented operation control strategy in a stand-alone power system in Greece are fully analyzed in the present study. A photovoltaic array and three wind generators serve as the system main power sources and meet a predefined load demand. A lead-acid accumulator is used to compensate the inherent power fluctuations (excess or shortage) and to regulate the overall system operation, based on a developed power management strategy. Hydrogen is produced by using system excess power in a proton exchange membrane (PEM) electrolyzer and is further stored in pressurized cylinders for subsequent use in a PEM fuel cell in cases of power shortage. A diesel generator complements the integrated system and is employed only in emergency cases, such as subsystems failure. The performance of the automatic control system is evaluated through the real-time operation of the power system where data from the various subsystems are recorded and analyzed using a supervised data acquisition unit. Various network protocols were used to integrate the system devices into one central control system managing in this way to compensate for the differences between chemical and electrical subunits. One of the main advantages is the ability of process monitoring from distance where users can perform changes to system principal variables. Furthermore, the performance of the implemented power management strategy is evaluated through simulated scenarios by including a case study analysis on system abilities to meet higher than expected electrical load demands.
Variable-speed pitch-controlled wind turbines with doubly-fed induction generators (DFIG) are modelled for power system dynamic stability. The model is explained and the parts of the model are verified. The model is implemented in the simulation tool PSS/E and created as a modular structure. This means that it is possible easily to add other control loops, as other modules, to the existing model code for ad-hoc investigations. This article is the first part of a large work dealing with investigation of dynamic interaction between the variable-speed wind turbines equipped with DFIG and the power grid.
The control strategy for a photovoltaic (PV) system with a hydrogen (H2) subsystem consisting of an electrolyzer, pressurized hydrogen gas storage, and fuel cell has been investigated. Detailed computer simulation models for TRNSYS have been developed, tested, and verified against a reference system, namely the PHOEBUS plant in Jülich, Germany. The basic control strategy and main logical control variables for a PV–H2 system are described. System performance indicators, parameters, and constraints that can be used to analyze the performance of PV–H2 systems have been identified. The results from a time series simulation for a typical year are presented. Finally, the importance of selecting smart control strategies is demonstrated.