Abstract – Nowadays, the power system utility has started to
consider the green power technology in order for the world to
have a healthier environment. The wind energy and solar
energy system are chosen in designing a hybrid renewable
power system as they do not release any emission to the
atmosphere. In addition, by maximizing the use of the
renewable energy, the diesel generator used in the system
could also be reduced. Therefore, this paper will discuss on the
optimization of the renewable energy hybrid system based on
the sizing and operational strategy of generating system. The
optimization software used in this analysis is the Hybrid
Optimization Model for Electric Renewable (HOMER). The
sensitivity analysis is also performed to obtain the optimal
configuration of hybrid renewable energy based on different
combinations of generating system.
Keywords: Hybrid renewable energy, HOMER, Solar Energy,
Wind Energy.
I. INTRODUCTION 1
Lately, human activities have given bad effects to the ecosystem and also the society. Scientists have proven that the act of releasing the greenhouse gases to the atmosphere has contributed to global warming. In addition, the materialistic lifestyle and industrialization have also caused to the environmental pollution. Hence, in order to overcome these problems, the renewable energy should be used as it holds the key to a healthy global environment. The major environmental problems like water pollution, ambient air quality (Carbon Dioxide, Nitrogen and Sulfur Dioxide gas emission), acid rain and global warming could also be solved. In Malaysia, energy policies and regulations are being enacted in order to stop the environmental problems as these policies are important in helping to achieve the goal of having sustainable development in Malaysia. The Malaysian government strategy is to maximize the use of indigenous energy resource and minimize the negative environmental impact. The energy efficiency and renewable energy under the Eighth Malaysian Plan (2001-2005) and the Ninth Malaysia Plan (2006-2010) are focused on targeting for renewable energy to be significant contributor and for better utilization of energy resources. An emphasis to further reduce the dependency on petroleum has also led to extra effort in integrating alternative source of energy [1]. In Malaysia, great attention is given to the solar and wind energy conversion (WES). The potential of wind energy is depending on the wind speed while the solar
1N.A.A. Razak, 1,*M.M. Othman, and 1I. Musirin are with the Faculty of
Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Malaysia. M.M. Othman can be reached at [email protected].
energy is affected by the solar resource that varies according to the location. Wind energy conversion and solar energy could have great potentials if being used by the resorts on the Islands of Malaysia especially at the East Coast areas. The objective of this paper is to determine the best configuration of hybrid renewable system referring to the optimal sizing and operational strategy of diesel generator, wind energy and solar energy that can offer the lowest amount of Total Net Present Cost (TNPC). The Hybrid Optimization Model for Electric Renewable (HOMER) software has been used to perform random selection of sizing and operational strategy of generating system in order to obtain the finest solution of hybrid renewable energy with lowest TNPC [2]. Thus, the information required for this analysis was collected based on the load profile, average monthly wind speed and solar radiation at the Pulau Perhentian Kecil, Terengganu in the year of 2004. The Pulau Perhentian Kecil is located at the East Cost area of Peninsular Malaysia a shown in Fig. 1. The wind speed in Malaysia is light and varies from one season another in the range of 2 m/s to 13m/s. The north east monsoon which is from the month of September to March plays important role in this region where the strongest wind comes from the South China Sea to the East Coast [1].
Fig. 1. Location of Pulau Perhentian Kecil, Terengganu.
II. METHODOLOGY
The proposed hybrid renewable energy system is comprising of wind turbine and Photovoltaic (PV) array system. Diesel generator with battery and power converter are added into the system as a backup unit and act as a storage system. This system is design specifically for an off
The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
grid system at remote area. The HOMER software is used to determine the optimal sizing and operational strategy for a hybrid renewable energy system based on three principal tasks which are simulation, optimization and sensitivity analysis. The following subsection discusses on the three principal tasks of the HOMER software.
A. HOMER: Simulation
HOMER simulates the operation of the system based on the components chosen by the designer. In this process, HOMER will perform the energy balance calculation based on the system configuration consisting several numbers and sizes of component. In this case study, PV array system, wind turbine, diesel generator with battery and converter are the components chosen for the analysis. It then determines the best feasible system configuration which can adequately serve the electric demand. HOMER simulates the system based on the estimation of installing cost, replacement cost, operation and maintenance cost, fuel and interest. B. HOMER: Optimization
The optimization process is done after simulating the entire possible solutions of hybrid renewable energy system configuration. HOMER display a list of configurations sorted based on the Total Net Present Cost (TNPC). It can be used to compare different types of system configuration from the lowest to the highest TNPC. However, the system configuration based TNPC is varied depending to the sensitivity variables that have been chosen by the designer.
C. HOMER: Sensitivity Analysis
The HOMER software will repeat the optimization process for every selection of sensitivity variables for the hybrid renewable energy system. The sensitivity variables are such as the global solar, wind speed and the price of diesel fuel. Then, the list of various configurations of hybrid renewable energy will be tabulated from the lowest to the highest TNPC. The optimal solution of hybrid renewable energy system is referring to the lowest TNPC.
III. INFORMATION FOR THE HYBRID RENEWABLE ENERGY SYSTEM
The aim of this case study is to carry out an optimal sizing and operational strategy of the hybrid renewable energy system. The load profile taken from Pulau Perhentian Kecil, Terengganu is used to simulate the whole operation for this system and it is shown in Table 1 [1]. This island is consisting of several resorts and one fisherman village which has about 100 family members. The latitude and longitude for this island are 5.91667 (5o 55’ 0 N) and 102.733 (102o 43’ 60 E), respectively. The life time estimated for this project is 10 years while the annual interest rate is fixed at 6%. Table 1. Load profile at Pulau Perhentian Kecil, Terengganu.
Season Occupancy
rate
Estimated daily
energy consumption
(kWh/day)
Max.
demand
(kW)
Peak season (March-July)
100% 1721 192
Low season (Aug-Feb)
60% 1031 115
A. Electrical load information
By referring to the load profile given in Table 1, 1253kWh/day is the average estimation of daily energy consumption (kWh/day) taken between these seasons. The energy load demand of this island is shown in Fig. 2. It is observed that the annual peak load of 197 kW is occurred in March. The large demand occurs during the peak season that is between March until July and the low demand happens between the low season that is from August until February.
Fig. 2. Hourly load variations in a year.
B. Photovoltaic (PV) economic information and solar
resource
The size of a PV array system that used in this system is 0.055kW. While the capital cost for each capacity is $510 = RM2,030.08 and the replacement cost is $480 = RM1,543.68. The solar radiation data is taken from Keniam, Pahang with the latitude of 4.52 N and longitude of 102.47 E. This island is close to the Pulau Perhentian Kecil with the latitude of 5.92 N and longitude of 102.73 E [3]. The time zone for this island is GMT +08:00. The array slope angle is set to 15o and the array azimuth is 0o which is referring to the South direction. The life time for this PV array system is 20 years with a derating factor of 90% and ground reflectance is 20%. The PV plant with a tracking system is neglected but the effect of temperature is considered. The clearness index and solar radiation are shown in Table 2 [3].
Table 2. Clearness index and solar radiation of a PV array system.
C. Wind turbine parameters & wind resource
The technical information of wind turbine is shown in Table 3. The information of average wind speed for Pulau Perhentian Kecil, Terengganu is shown in Table 4 [1]. The blade will not move if the wind speed is below than 5m/s and the blade will automatically stop when the wind speed is above 15m/s. The wind speeds over a year is presented in a Weibull distribution form shown in Fig. 3. The autocorrelation factor of r1=0.85 is measured based on the hour-to-hour randomness of the wind speed. The diurnal
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pattern strength of δ=0.26 represents as the strength of a
wind speed and the windiest time is ø=17.
Table 3. Wind energy technical information.
Table 4. Wind speed information.
Fig. 3. Weibull probability distribution function of wind speed. .
D. Diesel generator
The diesel power plant of 80 kW is installed in the island of Pulau Perhentian Kecil, Terengganu. The diesel generator information is shown in Table 5. The diesel price with four discrete values of 0.219$/L=RM0.83/L, 0.337$/L=RM1.28/L, 0.416$/L=RM1.58/L and 0.447$/L=RM1.70/L are used for the sensitivity variables. At present, the diesel price is about 0.5143$/L=RM1.95/L. The lower heating value is 45.62MJ/kg, density of the fuel is 831kg/m3 and carbon content is 80%.
Table 5. Diesel generator technical information.
E. Battery
The type of battery that used for the system is Hoppecke 24 OPzS 3000 model with the rating of 200V, 3000Ah, 6kWh. The cost for one battery is $1,890=RM6,070.68 with the replacement cost of $1,700=RM5,460.40. The battery stack is containing several numbers of batteries and the battery string is 120V(240V). F. Power converter.
A power electronic converter is used to maintain the flow of energy between ac and dc components [4]. The size of power converter used that used in this system is 1 kW. The capital cost and replacement costs for this equipment is $800=RM2,572.80 and $750=RM2,412.00, respectively. There are four different sizes of converter which are 60kW, 70kW, 90kW and 130kW considered in the design of hybrid renewable energy system. The lifetime for one unit of converter is 15 years with the efficiency of 90%.
IV. RESULTS & DISCUSSION The proposed hybrid renewable energy system for the
Pulau Perhentian Kecil, Terengganu is shown in Fig. 4. It consists of primary load which is 1253kWh/day, annual peak load of 197kW, PV system, wind turbine, diesel generator, battery and a converter serving for an AC electrical load. The HOMER software will identify the best possible configuration for the hybrid renewable energy system. For an example, the optimal sizing and operational strategy for a hybrid renewable energy system may sometime consider all of the equipments or without considering one part of the equipments. Thus, combination of the equipments is depending on the optimization procedure and sensitivity variables.
Fig. 4. PV-Wind-diesel hybrid system.
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4
Table 7. Optimization result with sensitivity variables.
Table 6. Optimization result without considering sensitivity variables.
A. Optimization of hybrid renewable energy system
HOMER performs the optimization process in order to determine the best solution of hybrid renewable energy ystem based on several combinations of equipments. Hence, multiple possible combinations of equipments could be obtained for the hybrid renewable energy system due to different size of PV array system, number of wind turbines, size of generator, number of batteries and size of dc-ac converter.
In the optimization process will simulate every combination system configuration in the search space. The feasible one will be displayed at optimization result sorted based on the Total Net Present Cost (TNPC). The combination of system components is arranged from most effective cost to the least effective cost. The optimization results of hybrid renewable energy system are obtained for every selection of sensitivity variables. Table 6 shows a list of optimization results for the hybrid renewable energy system without considering the sensitivity variables. The results represent different combination of components which are diesel generator, wind turbine, PV array system, battery and converter. However, sensitivity variables should be taken into account in order to obtain a rational result of hybrid renewable energy system. The average annual wind speed, diesel fuel price and solar radiation are the sensitivity variables considered for the optimal design of the system.
B. Hybrid renewable energy system considering sensitivity
variables
HOMER displays the average annual wind speed of 7.4m/s, fuel price of 0.447$/L=RM1.70/L and solar radiation of 2.8kWh/m2/d for the sensitivity variables shown in Table 7. It is then used in the optimization process to obtain the best configuration of hybrid renewable energy system consisting of wind turbine, diesel generator, PV array system, battery storage and/or power converter. In this
case study, the system consisting of wind turbine, diesel generator, battery storage and/or power converter yields to the most economical cost with the minimum TNPC of $997,085=RM3202,637.02 and a minimum Cost of Energy (COE) of 0.296$/kWh=RM 0.9508$/kWh. However, the TNPC and COE will become expensive when PV array system is included in the system. The energy obtained from different components of hybrid renewable energy system is shown in Fig. 5. The wind turbine produced 326,941kWh/yr that is 60% of the total energy served. The remaining 40% of total energy is served by the diesel generator which is 218,424kWh/yr. This system produced 11.3% of excessive energy which is 61.427kWh/yr and 0.1% of capacity shortage that is 404kWh/yr.
In Table 7, for the same sensitivity variables, the system consisting of wind turbine, diesel generator, battery storage and converter yields to a lower TNPC value of $997,085 compared to the rest of combined systems. However, the combination of diesel generator, battery storage and converter yields to a lower amount of excessive electricity that is 65.1kWh/yr or 0.0135% and this is shown in Fig. 6. This system produce a gas emission of 420,036kg/yr that is higher compared to a gas emission of 181,154kg/yr produced by the system consisting of wind turbine, diesel generator, battery storage and converter. The optimization result based on every combination of sensitivity variables is depicted in graphical form as shown in Fig. 7. The results show that every sensitivity variable gives different TNPC value of hybrid renewable energy system. It is worth mentioning that the sensitivity variables comprise of wind speed, solar radiation and fuel price. Fig. 7 shows that the TNPC of a hybrid system become economically feasible when the global solar radiation and wind speed is set above 2.8kWh/m2/d and 7.0m/s, respectively. These systems are consisting of wind turbine, diesel generator, battery storage and converter. It is
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Fig. 5. Energy obtained from wind turbine and diesel generator with excessive electricity of 11.3%.
observed that every TNPC of hybrid system is obtained based on a fixed diesel price of $0.447/L=RM1.70/L. On the other hand, the combination of diesel generation, battery storage and converter yields to a higher value of TNPC that falls under a region with very low wind penetration that is less than 7.0m/s. C. Green house gases (GHG) reduction
Recently, the world concerns on reduction of green house gases in order minimize the pollution of gas emission. The combination of wind turbine and diesel generator able to reduce the pollution of gas emission compared to a
system that only consisting of diesel system. Tables 8 and 9 show that 60% and 40% of electricity produced by the wind turbine and diesel generator, respectively able to reduce a significant amount of gas emission as compared to the system that only consisting of diesel generator.
Fig. 6. Energy obtained from diesel generator with excessive electricity of 0.0135%.
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Fig. 7. Optimal System Type (OST) with fixed diesel price = $0.447/L.
V. CONCLUSIONS This paper has discussed on the optimization sizing and operational strategy of hybrid renewable energy system which refers to the minimum cost of Total Net Present Cost (TNPC). The result shows that the combination of wind turbine, diesel generator, battery storage and converter brings to the optimal configuration of hybrid renewable energy system applicable to be used as an off-grid system in Pulau Perhentian Kecil, Terengganu. The following conclusions can be drawn from the results obtained from the analysis. There is a high potential of wind resource in Pulau Perhentian Kecil, Terengganu that can be used for supporting the renewable energy especially in terms of wind turbine compared to the solar energy. At present, a wind-diesel hybrid system has been chosen as the most suitable solution for the hybrid renewable energy system. The combination of wind turbine, diesel generator, battery storage and converter brings to the TNPC value of
$997,085=RM 3,202,637.02 that is lower than the TNPC of $1,033,092=RM 3, 318,291.50 for the hybrid renewable energy system consisting of diesel generator, battery storage and converter. The system found that PV array system will provide electricity during daytime. In order to provide the electricity at night, PV array system requires a battery storage and converter as an additional system and this will increase the cost TNPC. Therefore, this system is not applicable to be used as an off-grid system in Pulau Perhentian Kecil, Terengganu. On the other hand, the combination of wind turbine, diesel generator, battery storage and converter produce excessive amount of electricity that can be reduced by adding a large capacity of battery storage with converter. This will control the hybrid renewable energy system to produce electricity closely to the load consumption. Furthermore, the wind turbine serves 60% of total energy and this will reduce 57% of gas emission (CO2).
VI. REFERENCES
[1] Z.M. Darus, N.A. Hashim, S.N.A. Manan, M.A.A. Rahman, K.N.A. Maulud and O.A. Karim, “The Development Of hybrid Integrated renewable Energy System (Wind & Solar) for Sustainable Living at perhentian Island, Malaysia”, European Journal of Social Science, Vol. 9, No.4, 2009.
[2] Hybrid Optimization Model for electric Renewable Energy (HOMER), http://homerenergy.com/download.asp
[3] S. Shaari, K. Sopian and A.M. Omar, ‘Solar Irradiation Handbook For Photovoltaic Systyem Design in Malaysia’, Solar Energy Research Institute, Universiti Kebangsaan Malaysia.
[4] M.J. Khan and M.T. Iqbal, “Pre-feasibility study of stand –alone Hybrid Energy System for Application in Newfounland”, Renewable Energy, Vol. 30, pp. 835-854, 2005.
VII. BIOGRAPHIES
Nurul Arina bte Abdull Razak obtained the B.Eng. (Hons) degree from Universiti Teknologi MARA, Shah Alam, Malaysia, in 2010. His area of research interest is in optimal sizing and operational strategy of hybrid renewable energy system; and distributed generation.
Table 8. Green House Gases (GHG) for diesel generator.
Table 9: Green House Gases (GHG) for wind turbine and diesel generator.
.
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The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010
Muhammad Murtadha bin Othman received the B.Eng. (Hons) degree from Staffordshire University, U.K., in 1998; the M.Sc. degree from Universiti Putra Malaysia, Serdang, Malaysia, in 2000 and Ph.D. degree from Universiti Kebangsaan Malaysia, Bangi, Malaysia, in 2006. He is currently the Chair, Centre of Electrical Power Engineering Studies (CEPES), Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia. His
area of research interests are artificial intelligence, transfer capability assessment and reliability studies in a deregulated power system. He is a member of IEEE.
Ismail bin Musirin obtained the Diploma of Electrical Power Engineering in 1987, Bachelor of Electrical Engineering (Hons) in 1990; both from Universiti Teknologi Malaysia, MSc in Pulsed Power Technology in 1992 from University of Strathclyde, United Kingdom and PhD in Electrical Engineering from Universiti Teknologi MARA, Malaysia in 2005. He is currently the Deputy Dean of Research and Industry Network, Faculty of Electrical Engineering, Universiti Teknologi MARA,
Shah Alam, Selangor. His area of research interests are artificial intelligence, voltage stability studies, and application of microgrid and distributed generation in power system.
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