Aalborg Universitet
Mixed-Integer-Linear-Programming Based Energy Management System for Hybrid PV-
wind-battery Microgrids: Modelling, Design and Experimental Verification
Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Graells, Moises;
Quintero, Juan Carlos Vasquez; Guerrero, Josep M.
Published in:
I E E E Transactions on Power Electronics
DOI (link to publication from Publisher):
10.1109/TPEL.2016.2581021
Publication date:
2016
Document Version
Early version, also known as pre-print
Link to publication from Aalborg University
Citation for published version (APA):
Hernández, A. C. L., Aldana, N. L. D., Graells, M., Quintero, J. C. V., & Guerrero, J. M. (2016). Mixed-Integer-
Linear-Programming Based Energy Management System for Hybrid PV-wind-battery Microgrids: Modelling,
Design and Experimental Verification. I E E E Transactions on Power Electronics. DOI:
10.1109/TPEL.2016.2581021
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www.microgrids.et.aau.dk
Mixed-Integer-Linear-Programming Based Energy
Management System for Hybrid PV-wind-battery
Microgrids: Modelling, Design and Experimental
Verification
Adriana C. Luna, Student, IEEE, Nelson L. Diaz, Student, IEEE, Mois
`
es Graells, Juan C. Vasquez, Senior, IEEE,
and Josep M. Guerrero, Fellow, IEEE
Abstract—Microgrids are energy systems that aggregate dis-
tributed energy resources, loads and power electronics devices
in a stable and balanced way. They rely on energy management
systems to schedule optimally the distributed energy resources.
Conventionally, many scheduling problems have been solved by
using complex algorithms that, even so, do not consider the
operation of the distributed energy resources. This paper presents
the modeling and design of a modular energy management system
and its integration to a grid-connected battery-based microgrid.
The scheduling model is a power generation-side strategy, defined
as a general mixed-integer linear programming by taking into
account two stages for proper charging of the storage units.
This model is considered as a deterministic problem that aims to
minimize operating costs and promote self-consumption based on
24-hour ahead forecast data. The operation of the microgrid is
complemented with a supervisory control stage that compensates
any mismatch between the offline scheduling process and the
real time microgrid operation. The proposal has been tested
experimentally in a hybrid microgrid at the Microgrid Research
Laboratory in Aalborg University.
Index Terms—Power generation scheduling, Energy manage-
ment, Integer programming, Dispersed storage and generation.
I. INTRODUCTION
M
ICROGRIDS (MG) integrate and manage Distributed
Energy Resources (DER) by ensuring a reliable and
stable operation of the local energy system either when the
MG is connected or disconnected to the main grid. A MG
can aggregate different kinds of DERs, such as distributed
generators and distributed storage, loads and power electronics
devices and grid components [1], [2]. For an interactive
operation of DERs, an Energy Management System (EMS)
is required to coordinate their operation within the MG [3].
The EMS provides reference profiles for the controllers of the
MG in accordance to predefined objectives [4]–[6].
A. C. Luna, J. C. Vasquez and J. M. Guerrero are with the
Department of Energy Technology, Aalborg University, 9220 Aal-
borg, Denmark, e-mail: acl@et.aau.dk, juq@et.aau.dk, joz@et.aau.dk (see
http://www.microgrids.et.aau.dk).
N. L Diaz with the Department of Energy Technology, Aalborg University,
9220 Aalborg, Denmark and also with the Faculty of Engineering, Uni-
versidad Distrital F.J.C., 110231 Bogota, Colombia, email: nda@et.aau.dk,
nldiaza@udistrital.edu.co
M. Graells is with the Department of Chemical Engineering , Uni-
versitat Polit
`
ecnica de Catalunya, 08036 Barcelona, Spain, email: moi-
ses.graells@upc.edu
The energy scheduling problem for providing commands
to power electronics devices has been addressed in previous
works, such as [7]–[9], but only in the framework of reactive
approaches (the actions are determined based on current ope-
rational conditions). On the other hand, scientific contributions
focusing on the optimization problem do not consider the
operation modes of the controllers and devices [2], [10], [11],
or lack of experimental validation [12]. In this way, it is still
missing the modelling and experimental implementation of an
optimal scheduling in a microgrid that considers the demand
and the availability of energy in short term, as well as the
operation modes of the power electronics devices.
Several models for MG optimization have been proposed
including heuristic methods such as genetic algorithms, parti-
cle swarm optimization and game theory [13]. Those methods
do not guarantee the global optimal solution and may be
inefficient and time-consuming [14]. Linear and dynamic
programming methods ensure the optimal solution when the
solution is feasible. But, they typically consider the Renewable
Energy Sources (RESs) just as non-dispatchable sources (input
data), and, accordingly, Energy Storage Systems (ESSs) are
scheduled for balancing generation and demand [2], [10], [11].
The use of ESSs in MGs demands additional technical
requirements within the EMS. Especially, those based on bat-
teries need a proper management of the State of Charge (SoC)
in order to prevent fast degradation. Therefore, the ESS should
be accompanied by a battery charge control which avoids
overcharge and deep discharge of the battery. Meanwhile, the
EMS is responsible of scheduling properly the DERs, seeking
for a proper window of stored energy, and a reduction in the
stress caused by repeated cycles of charge [15]. An optimal
energy storage control strategy for grid-connected MGs is pre-
sented in [12] where a Mixed Integer Linear Program (MILP)
optimization is used to solve an economic problem but the
results are not validated experimentally. Besides, in previous
works, [16] and [12], the authors do not consider the fact
that the ESS can get fully charged during the time horizon. In
[17], an energy management strategy is proposed for operating
photovoltaic (PV) power plants with ESS in order to endow
them with a constant production that can be controlled. In
that work, the optimization aims to keep the SoC level of the
battery as close as possible to a reference value at all times.
Nevertheless, keeping the SoC of batteries in a fixed level is
2
Fig. 1. Microgrid site in Shanghai
not the best practice since battery manufacturers recommend
to charge completely the batteries between discharges cycles
in order to improve their performance [15]. In [18], the authors
consider periods of full charge of the battery as well as reduced
stress in discharge cycles of the battery by limiting the deep
of discharge (DoD) to 30%. The main disadvantage of this
approach is that the ESS is fully charged from the main grid
at the beginning of the operation day instead of using the
surplus of power from the renewable energy generators.
In the case of small-scale microgrids, the current trend is
oriented to promote local consumption of the energy generated
by RESs rather than exporting the surplus of electricity to
the main grid [19]. This is specially important because under
periods of high generation and low local consumption, the
surplus of power generated from RESs and fed-in to the main
grid may cause significant variations in the voltage at the
common coupling point [20]. In order to ensure voltage quality
in the grid-connected microgrid, the surplus in RES generation
should be limited when there is not enough storage capacity in
the ESSs [20]. In this sense, [21] defines the term connected
islanded mode in which the MG is connected to the grid but
the management is performed to avoid power exchange with
the utility. One strategy to deal with this issue is by means
of power curtailment of the RES generation [22]. In [23], this
alternative is used to limit the power injected to the main grid.
In [20] authors develop a power control strategy by limiting the
maximum power injected by PV systems, ensuring a smooth
transition between maximum power point tracking (MPPT)
mode and Constant Power Generation.
In this paper, a flexible structure of EMS for battery-based
hybrid microgrids is designed and experimentally tested to
provide optimal power references for DERs by considering
their operation modes. The EMS includes the modelling of
an optimization problem that aims to minimize operating
costs, taking into account a two-stage charge procedure for
ESSs based on batteries. In this way, the power delivered to
the grid is limited while safe operation ranges of ESSs are
ensured, which in turn, avoids their fast degradation [15]. The
mathematical formulation is straightforward, reproducible and
can be used and enhanced to other microgrids. The MG is
complemented with the design of a fuzzy-based supervisory
control level that reacts to the deviation of the utility power
by adjusting the references of the DERs. This supervisory
control level can also work without the EMS to provide power
references in a reactive mode. The experimental verification
is performed under the particular case of grid connected con-
dition and promoting self-consumption (connected islanded
mode) based on 24-hour ahead prediction.
The paper is organized as follows: Section II describes the
operation of the MG defined as case study, Section III includes
the modelling of the optimization problem, Section IV presents
the experimental results and Section VI concludes the paper.
II. MICROGRID OPERATION
The MG selected as case study is a lab-scale prototype of a
real microgrid platform implemented in Shanghai, China (Fig.
1) [24]. The MG consists of two RES (a wind turbine (WT)
and a PV generator), each one with a power rating of 1.2 kW,
a battery-based ESS, a variable load and a critical load. The
MG is connected to the main grid through a transformer as
shown in Fig. 2.
Since the MG is grid-connected, the main grid imposes the
conditions of the common bus (voltage and frequency) and
manages any unbalance between generation and consumption.
Meanwhile, DERs work as grid-following units, which are
synchronized with the main grid at the connection point
in order to exchange properly the power defined for each
unit within the MG and with the main grid [25]. In this
sense, the power references can be defined by grid non-
interactive or grid-interactive operation strategies [4]. Namely,
grid-noninteractive operation means that the power reference
of the unit is determined locally without considering a prede-
fined power set point (non-dispatchable source). For instance,
the operation of RESs, which follows MPPT algorithms or
regulated charge of the ESS, can be considered as grid-
noninteractive operation. On the other hand, grid-interactive
operation means that the DERs will follow a power value
provided by the supervisory control after adjusting the defined
references given by the EMS (dispatchable source).
The supervisory control manages the deviation between
the reference and measurement of the utility power due to
the variability and prediction errors of RESs by adapting the
references of the DERs, whenever possible. The implemented
strategy is based on a fuzzy inference system that adjusts the
set-points of DERs considering the SoC of the battery so that
the power profiles scheduled for absorbed power from the grid
can be followed.
3
Fig. 2. Structure of the battery-based MG defined as case study.
The power references for the supervisory control are pro-
vided by the proposed EMS which is composed of four
modules, optimization, data processing, user interface and
data storage. The optimization model is implemented in the
module optimization by means of an Algebraic Modelling
Language (AML) that automatically translates the problem
so that the solver can understand it and solve the problem.
The input and output data is structured by the data processing
model and stored in the data storage which is a collection
of files accessible by the user interface and the supervisory
control.
A. Local Controllers
In particular, the case study considered in this paper is
mainly focused on grid-connected operation of the microgrid.
Because of that, the control loop of all the DERs (ESS and
RES) can be unified as shown in Fig. 3 where, the inner
current control loop regulates the current injected or absorbed
from the main grid [26]. Additionally, the current control loop
is complemented with a current reference generator which
generally defines the feed-forward reference signal I
∗
dq
(in d-q
reference frame) as a function of the active and reactive power
references P
∗
and Q
∗
, and the output voltage of each DER
V
Cdq
as
i
∗
d
=
2
3
P
∗
v
Cd
, i
∗
q
=
2
3
Q
∗
v
Cd
(1)
where, v
Cd
is the d-component of V
Cdq
, and the q-component
v
Cq
= 0 since each DER is synchronized with the voltage at
the common coupling point V
P CC
by means of a phase lock
loop (PLL) [27].
For the proposed management of the microgrid, all the
DERs can operate in noninteractive or grid-interactive mode
in accordance to particular operational conditions of each unit.
There are some differences that should be considered between
the operation of the ESS and the RESs [25], [28].
1) Operation of RESs: RESs are more likely to operate by
following a MPPT algorithm but, under certain conditions, it
is required to limit the active power generation in accordance
to optimization objectives defined by the EMS [22], [29].
Because of this, the active power reference P
∗
should be
defined as the minimum value between the power reference
established by the MPPT algorithm (P
MP P T
) and the power
reference scheduled by the EMS P
sch
. In this way, it is
possible to achieve the curtailment in the generation of RESs
(grid-interactive operation) when (P
sch
< P
MP P T
), or ensure
the maximum possible generation (noninteractive operation)
in the case that (P
MP P T
< P
sch
). Fig. 4 shows a simplified
scheme of the power reference selection for RESs where the
power reference is defined as P
∗
= min(P
MP P T
, P
sch
). It is
worth to say that MPPT algorithms are out of the scope of this
paper. Interested reader may refer to [30] and [31]for MPPT
strategies applied to PV and WT generators respectively.
RESs commonly use a multi-stage converter in which one of
4
+
-
P
dq
abc
PWM
dq
abc
a
v
b
v
c
v
lb
i
lc
i
1b
C
1c
C
Cc
v
Cb
v
Ca
v
la
i
a
L
b
L
c
L
2b
L
2c
L
1a
C
n
dq
I
Cdq
V
lb
i
lc
i
la
i
2a
L
Power
Source
To
AC BUS
PLL
θ
PLL
θ
dq
Current
Controller
Current
Reference
Generator
eq (1)
*
dq
I
Cdq
V
dq
abc
PLL
θ
PLL
PLL
θ
*
P
*
Q
Inner Loops
Grid-Side
Converter Control
Grid-Side Converter
PCC
V
Fig. 3. Current Control loop for DERs.
Current
Reference
Generator
eq (1)
*
P
*
Q
Cdq
V
min
sche
P
MPPT
P
*
d
i
*
q
i
*
dq
I
grid-interactive
grid-noninteractive
Fig. 4. Current Reference Generator for RESs.
them is mainly responsible of the regulation of an intermediate
dc-link while the other follows the power reference. In this
application, the power reference is regulated by the grid side
converter (see Fig. 3) and the intermediate dc-link is assumed
as regulated by the first conversion stage. Because of that, it is
possible to consider RESs as a power source just as is shown
in Fig. 3.
2) Operation of ESS: When the ESS is based on batteries, a
two-stage charge procedure is recommended for charging them
in order to limit excessive overcharge of the battery array [15],
[32]. Fig. 5 represents the stages for the operation of the ESS.
In the first stage (limited current charge), the ESS is in grid-
interactive operation and injects or absorbs active power in
accordance to the power reference (P
ref
bat
). When the voltage
per cell in the battery array reaches a threshold value (V
r
),
known as the regulation voltage (typically 2.45 ±0.05 V/cell),
the battery voltage should be limited to this value while the
current at the battery approaches to zero asymptotically. In
this case, the ESS switches to a grid-noninteractive operation
(voltage charger mode) in which the ESS extracts a small
amount of power from the system in order to ensure a constant
voltage charge [33], [34].
The transitions of the ESS between operation modes (nonin-
teractive and grid-interactive) are managed by a local sequen-
R
Fig. 5. Charger stage of a battery
tial logic unit. Once the battery voltage reaches the threshold
value (V
r
), the local unit triggers the transition between inter-
active to grid-noninteractive operation. Similarly, the logic unit
returns the operation of the ESS to grid-interactive operation
on request (P
ref
bat
> 0). Fig. 6 shows the current reference
generation block for the ESS, including the transition table of
the sequential logic unit. In the transition table, X indicates that
the value of the variable is unimportant. The transition between
operation modes depends of the current operation mode (S
where 1 represents activated state), and the logic value of
the inputs (P
ref
bat
> O and V
bat
= V
r
). It is possible to see
