Studying the Effect of Decentralized Battery Storage to Smooth the

Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt, December 19-21, 2010, Paper ID 253.
Studying the Effect of Decentralized Battery Storage
to Smooth the Generated Power of a Grid Integrated
Wind Energy Conversion System.
Mohamed Ibrahim, Amr Khairy, Hani Hagras, Mina Zaher, Abdellatif El Shafei, Adel Shaltout, and
Naser Abdel Rehim.
Abstract— this work investigates the technical possibility of
using battery storage in order to smooth the power generated
from a grid connected wind energy converter unit. Wind
energy has gained much credit in the past two decades as a
sustainable energy resource. The penetration of wind energy
generators into the electric utility grids is expected to increase
to about 203.5 GW within the present decade. Due to the
intermittent nature of the wind and the limited reliability of the
wind prognoses there have been serious concerns about
reliability and operation of the utility power grids. Battery
storage is suggested to compensate wind power fluctuations and
smooth the power flow to the utility grids. The battery storage
in such applications has dynamic operating conditions and is
subjected to different degradation mechanisms which stimulate
the capacity losses and hence influence the feasibility of their
implementation. In this paper, the real behavior, the technical
feasibility of the battery and its effect on wind power fed to the
utility grid will be judged. The investigated system is simulated
using real measurement data of a 600 kW rated power wind
turbine. The simulation results of different battery capacities
show that the integration of the battery storage has
compensated the fluctuations of the generated wind power to
match the forecasting value, which smoothed the power fed to
the utility grid and allows better grid operation. Moreover, the
battery aging model has generated very important information
about the battery degradation and available capacity (in this
case of about 85%) after one year of operation. Therefore,
further investigations with different battery technologies (e.g.
Li-Ion and NiMH) and development of intelligent system
operation strategy have to be investigated.
T
I. INTRODUCTION
HE previous two decades have witnessed great interest
in the renewable energy resources as sustainable
solutions for the worldwide battle against climate change
and reducing the harmful emissions accompanied with
conventional fossil fuel based energy generation. Fossil fuels
are eventually destined to extinction and the recent years
M. Ibrahim and A. Khairy are with the Faculty of Technology and
Energy Management, Heilbronn University, Daimlerstr. 35, D-74653,
Kuenzelsau, Germany (e-mail: [email protected])
H. Hagras is with the German University in Cairo, Egypt, he is also with
the Computational Intelligence Centre, School of Computer Science and
Electronic Engineering, University of Essex, Wivenhoe Park, Colchester,
CO4 3SQ, UK (e-mail: [email protected])
M. Zaher is with the German University in Cairo, Cairo, Egypt.
Abdellatif El Shafei and Adel Shaltout, are at the Dept. of electrical
power engineering, Cairo University, Cairo Egypt
Naser Abdel Rehim is at the Dept. of electrical power engineering,
Banha University, Banha Egypt
have witnessed strong technological advancements and
continuously decreasing prices of renewable energy
generation, especially by wind energy generators (WEGs).
Currently, wind driven electricity generators are the most
dominant renewable converters that are integrated to the
utility grids. The modular and decentralized nature of the
WEGs makes them an attractive solution to many regional
power suppliers. All wind turbines installed by the end of
2009 worldwide generate 159 GW and produce 340 TWh
per annum, equalling 2 % of global electricity consumption,
refer to Fig. 1 [1].
Fig. 1. Worldwide installed wind power capacity [1].
The dependency of the WEGs on the continuously
varying wind speed enforces many technical challenges to
the process of their integration to the utility grids. the output
power of total installed WEGs in Germany can vary
seasonally by about 70% [2].
These fluctuations in the generated wind power should be
compensated by the utility grid operator in order to assure a
stable and reliable power supply. For this compensation
process, several techniques are adopted, mainly by
controlling of local or regional conventional power stations,
or even by purchasing energy of unpredictable dynamic
price from the market [3]. This fluctuation compensation
process is combined with energy loss due to transformation
of the controlling power to long distances. Moreover, the
expected high penetration of WEGs connected to the utility
grid will cause huge stresses enforced to the grid operation.
The use of fast and responsive energy storage media to
compensate the power fluctuations from WEGs will result in
smoother power output from wind farms. The role of the
energy storage in grid connected WEGs is to store a part of
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the generated wind power which exceeds a predefined level.
This stored energy will be supplied back to the grid if the
generated wind power is less than the predefined level. This
smoothing approach reduces strong wind power fluctuations
fed into the utility grid.
Different storing capacities and technologies have been
investigated by [4]-[8] for short periods (seconds) as well as
for long periods (several hours). An intermittent storage
medium such as the battery represents a cost-effective and
feasible solution, since it can be installed beside the WEG
and reduces the energy losses caused by long transformation
distances [5].
During the operation of the battery storage in continuous
charging and discharging processes, different stresses are
exerted to the battery structure (plates, grid, and electrolyte)
which accelerate its degradation and hence shorten its
lifetime. Investigation carried out by [9], [10] shows that
battery lifetime varies between 2 to 4 years in renewable
systems. Accordingly, high costs of battery maintenance and
replacement will be unfeasible. Therefore, the battery
degradation has to be considered when operating with
WEGs.
Battery performance analyses under different conditions
have been presented by [11], [12]. Many difficulties are
assigned to the estimation of the actual battery capacity and
its internal state of health during operation. A quasi-static
battery characteristics model will result in unreliable control
strategies as has been indicated by [11]. Also, mathematical
modelling of the degradation mechanisms was evaluated by
[13]. However, most of the mathematical modelling
techniques require many set parameters and do not cover the
most critical ageing effect in the battery, which is the
capacity degradation.
Fuzzy agents can be regarded as knowledge-based
management techniques offering a rich environment for
modelling and investigating the battery capacity degradation
in WEG systems. Previous investigation in modelling the
battery using fuzzy logic has been carried out, for example,
by [14] where the dynamic performances of the battery
voltage and state-of-charge (SOC) have been investigated.
However, this work does not consider the battery
degradation which is essential if the reliability in renewable
energy applications is to be considered [10].
This work investigates the usage of the lead-acid battery
storage for compensating the power fluctuation of a grid
connected WEG. The battery performance will be
investigated using fuzzy agent as a degradation estimator
model of the battery. Many years of experience in battery
operation are available and documented in several studies.
This knowledge will be used to develop the fuzzy agent and
its performance in WEG systems will be introduced.
Section II discusses the integration of WEGs to utility
grids and highlights the accompanied technical challenges.
Section III discusses the development of the fuzzy-agent
used in modeling the battery capacity degradation, as well as
the case study with real data and simulation results using
different storage capacities connected to the used WEG.
Finally, conclusion and future work are presented in the final
section
II. GRID INTEGRATION OF WEGS
As the amount of wind energy in the electricity grid
increases, new challenges emerge concerning the grid
operation and power flow stabilization. Initially built for
traditional power sources, the grid is not yet fully adapted to
the foreseen levels of wind energy, and nor are the ways in
which it is designed and operated [15].
Wind power variation can be treated as variation in the
load demand, not only more frequent but also more difficult
to predict [16], [17]. Owing to the present limitations in
wind speed forecast, the actual values for wind power can
differ from the forecasted ones by 5 to 20%, and therefore
posing challenges for the primary and secondary control of
the power system.
Due to the advancements in power electronics (e.g.
frequency converters) during the last two decades, the
supplied voltage and frequency at the grid side as well as the
wind turbines’ points of connection can be kept within the
power system standards [8].
In the case of stiff grids, penetration levels of wind energy
generation up to 20-30 % can be tolerated without disturbing
their stability [7]. As the penetration levels are expected to
increase all over the world, reliable solutions have to be
enforced in order to smooth the power fluctuations from
grid-connected WEGs. Energy storage mechanisms are
proposed as a solution to the future problem.
Fig. 2 WEG system topology integrated with battery storage and the
monitoring and diagnosis fuzzy agent.
A short-term energy storage element (super-capacitor) in a
DFIG operated WEG has been investigated by [6]. The
super-capacitor offers storage ranging up to a few minutes.
This configuration proved to enhance the performance of the
WEG during transients and low-voltage ride-through
periods.
Lead-acid (Pb) and nickel-cadmium (NiCd) batteries are
dominant for their well-established technologies and tested
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behaviour. In [7], a number of studies were used to prove
that energy storage with several minutes of storage capacity
is optimum for stabilizing wind generation in weak grids.
However, this study did not include the degradation of the
battery which can potentially influence its feasibility in such
applications.
This paper proposes the introduction of an AC-coupled
battery parallel to the wind turbine, as shown in Fig. 2.
Generally the battery can contribute to the reduction of the
fluctuations in the wind power fed to the grid. When the
actual speed varies from the forecasted value, the battery can
be charged or discharged to decrease or even eliminate the
difference between the predicted and actual generated wind
powers.
III. BATTERY AGEING MODEL AND POWER BALANCE
A.
Development of the Fuzzy Agent for Capacity Loss
Estimation
Three main mechanisms are responsible for capacity
reduction in the lead-acid battery, namely grid corrosion,
sulphation of the negative electrode, and sulphation
accumulation.
Battery ageing estimation model is developed and verified
using fuzzy-logic algorithms. The process involves different
sub-modules of fuzzy logic [18]. Sample of the Fuzzy-RuleBased is given in Fig. 3.
The measured data is 5 minutes average wind speed at
30 m height and the corresponding generated wind power
from March 2003 to February 2004. Output power from the
turbine was considered as the actual instantaneous power
produced by the turbine, i.e. power fed to the grid if the
battery were not installed. The forecasted wind speed and
hence the output power forecast were not available for the
case study, therefore; the forecasted output power was rather
calculated.
C. Simulated battery capacity
The simulated battery capacities are chosen to range from
5- to 30-minutes of the rated WEG power. At the end of the
year, the performance of the battery is tested with respect to
its actual capacity (in percentage of the initial capacity) and
the average state of charge (SOC) as the degradation
development criteria.
D. Wind-battery power balance
The aim of the power management strategy is to keep the
wind power fed to the grid equal to the forecasted value. The
battery is used as a buffering medium to compensate the
differences between the actual and the forecasted output
powers. The expected (forecasted) output power throughout
a certain period of time is calculated as the average of the
available measured values during this period. In the present
case study, we shall present the influence of calculating the
average value from one, two and four hours of readings, and
taking it as the forecasted value during the chosen period.
The power balance between the wind turbine, battery
storage and the utility grid is formulated as follows:
Pgrid = Pwind + PChg / Disch
(1)
Pexcess / shortage = Pexp ected − Pwind − PChg / disch
(2)
where, PChg/Disrch is the power charged (-ve) or discharged
(+ve) into /from the battery, PExcess/Shortage is the
excess /shortage energy spilled into or deprived from the
gird. PExpected is the forecasted power, i.e. calculated mean
value, and PGrid is the power fed from the whole system to
the electrical power system.
IV.
Fig. 3. The overall input/output surface of the fuzzy-agent rules.
B. WEG System and Measurement Specifications
The grid connected WEG integrated with battery storage,
as shown in Fig. 2, is modeled and simulated via
MATLAB/Simulink®. Measured data used in the present
case study are taken from a wind turbine located at Luckau
in the East of Germany. The investigated wind turbine is of
the type E-40/6.44 provided by the company Enercon. The
turbine has a rated power of 600 KW, a rotor diameter of 44
meters and a 78 meter hub high. The E-40/6.44 is a gearless
turbine that adopts the variable-speed operation topology
and is connected to the grid through a synchronous
generator.
RESULT ANALYSIS
The simulation is conducted for different storage and
different forecasted average powers as mentioned above.
The performance of the used battery was investigated with
respect to the actual capacity after one year of operation, in
comparison to the initial capacity. The results presented in
Fig. 4 show that batteries with smaller storage capacities (5
minutes of storage) have outperformed other batteries with
higher capacities (10 to 30 min). The reason for this
behavior is shown in Fig. 5 which shows that batteries with
lower capacities have a higher average SOC and therefore
spend less time in deep discharge conditions.
Fig. 6 and 7 show the excess and shortage energy,
respectively, in percentage to the total energy available from
the WEG. It’s obvious that the percentage of shortage and
excess power decreases as the battery capacity increases.
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Good performance is proven by the battery of high capacity;
shortage energy is shown to be less than 2 % whereas the
excess energy is almost 0.5 %.
One week of system operation is shown in Fig. 8. The
forecasted output power was assumed to remain constant
along each hour of operation and was calculated as the
average value of 5 minutes measurements. The power
actually fed to the utility grid is compared to that forecasted
by the grid operator. For the case of a WEG without storage,
shown in Fig. 8-a, the intermittent nature of the WEG power
fed to the grid can be noticed clearly. However, by adding 5or 30-minutes of storage capacity, the WEG power fed to the
grid is strongly smoothed as shown in Fig. 8-b and -c. As
battery storage is integrated, the power fed to the grid is seen
to follow the expected power from the utility grid operator to
a very high extent throughout the week. This will simplify
the grid operation, since the forecasted wind power can be
more reliably scheduled by the grid management. Moreover,
the deviation from the forecasted value at some times during
the week can be compensated with intelligent decentralized
management.
However, the quick capacity degradation of the battery is
a contra effect. Further phases in this work will be dedicated
to investigate different battery technologies such as NiMH or
Li-Ion. Moreover, intelligent management strategies will be
investigated for improving the battery performance and
compensating generation deviation.
V. CONCLUSIONS
This paper investigated the performance of a lead-acid
battery in scheduling wind power flow to the utility grid. In
this work, the performance of a WEG is simulated using a
real measurement of the E40/6.44 wind turbine with rated
power of 600 kW. Also, different sizes of the Pb-battery
storage and its sophisticated ageing mechanisms have been
considered in the investigation. The simulation results
showed that smaller storage sizes (about 5-minutes of wind
rated power) and longer forecasting steps of wind power
Fig. 4. Actual capacity after one year of operation; different battery
capacities and different average values.
Fig. 6. Excess of energy under different storage capacities.
Fig. 7. Shortage of energy under different storage capacities.
(about 4-hours ahead) have the minimum degradation
influence upon the battery capacity. However, in order to
achieve high scheduling reliability of the wind power fed to
the utility grid, larger storage sizes (about 30-minutes of the
rated wind power) are expected. This can simplify the
scheduling of the secondary and primary regulating power
stations, since the forecast of the wind generation is of a
higher reliability with using the storage medium.
Accordingly, cost reduction of the reserve power capacities
and more reliable grid operation are expected. However,
huge capacity degradation detected by the ageing
mechanisms of the battery is the consequence. Further
investigations will target different battery technologies (e.g.
Li-ion and NiMH) and intelligent decentralized management
techniques.
ACKNOWLEDGMENT
The authors would like to acknowledge the support of the
German International Office of the Federal Ministry of
Education and Research (IB-BMBF) and the Egyptian
Science and Technology Development Fund (STDF) for cofinancing the project entitled “Evaluation of Fuzzy Control
Algorithms in Wind Energy Conversion Systems”, STDF
599 (EGY-08-045). The authors would like also to thank
Fraunhofer-Institute for Wind Energy and Energy System
Technology (IWES) for the wind measurements.
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(a)
(b)
(c)
Fig. 8. Power fed to the grid for: (a) WEG without battery, (b) with 5-miutes storage capacity, and (c) with 30-mintes storage capacity
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