Open access peer-reviewed chapter

# Optimization of Hybrid Energy Efficiency in Electrical Power System Design

Written By

Kenneth E. Okedu, Roland Uhunmwangho, Ngang Bassey Ngang and Richard Azubuike John

Submitted: June 18th, 2014 Reviewed: August 25th, 2014 Published: April 22nd, 2015

DOI: 10.5772/59017

From the Edited Volume

## Energy Efficiency Improvements in Smart Grid Components

Edited by Moustafa M. Eissa

Chapter metrics overview

View Full Metrics

## 1. Introduction

Evaluation of economic and technical feasibility of a large number of technology options, accountability for variations in technology costs and energy resource availability, could easily be carried out using the hybrid optimization model for electrical renewable (HOMER). A power system designer can use HOMER to provide an important overview that compares the cost and feasibility of different configurations and evaluate the technical performance of the power system [1]. A hybrid system is an electricity generation system, based on the integration of various energy sources (such as photo voltaics, wind turbines, small hydro power or diesel generators) [2]. Hybrid configurations can potentially deliver improved performance and better economic values for a given electrification situation [3].

Among the various energy modeling software available, the capabilities provided by the HOMER software is the best option for modeling and investigating various hybrid systems. The program first runs an hourly simulation of all possible configurations of system types. Due to the speed of processing these simulations, there is room for the evaluation of thousands of combinations. This hourly simulation also provides improved accuracy over statistical models that typically evaluate average monthly performance of a system. HOMER also models the partial load efficiency of diesel generators. This more accurately simulates the lower efficiency of a generator when it is not operating at full capacity. When the simulations have be run, HOMER sorts the feasible cases in order of increasing net present (or lifecycle) cost. This cost is the present value of the initial, component replacement, operation, maintenance, and fuel costs. HOMER lists the optimal system configuration, defined as the one with the least net present cost, for each system type. The sensitivity analysis of HOMER then repeats this optimization as user-defined factors, such as fuel price, load size, reliability requirement, and resource quality [4, 5]. Furthermore, the HOMER analysis simplifies the task of evaluating designs of both off-grid and grid-connected power systems for a variety of applications. In designing a power system, many decisions about the configuration of the system are to be made: components to include in the system design, size of each component to use etc. The large number of technology options and the variation in technology costs and availability of energy resources make these decisions difficult [6].

In [6-8], the authors limited the use of the HOMER software to only the solar resources, while in [8, 9], an analysis was carried out proposing an optimization solution of a hybrid system of renewable energy by using the Homer software for remote areas. The Hybrid systems reported in these papers involve combination of different energy sources like wind/battery, PV/battery, wind/PV/battery, wind/PV /diesel/battery. However, various sizes of the system configurations were not taken into account and the focus was not on the best operating conditions and combination of the power systems, in terms of optimized energy efficiency. This chapter presents the use of HOMER software in the analysis of a power system comprising a wind turbine, solar photo voltaic, AC generator, converter, primary load and battery system. Various sizes of the sources were considered for all possible configurations of system types. The optimized energy efficiency based on the least net present cost was used as the basis for the selection of the best operating condition of the power system. Also, a further investigation was carried out considering two cases with two different load profiles to show that the load profiles affects the responses of the renewable energy system and the cash flow summary of some of the system equipments. In light of this, a wind turbine is integrated into the PV, battery, converter and AC diesel generator system.

## 2. System model considered

The model system used for this study is shown in Figure 1, where a primary load of 157kwh/d, 22kW peak is connected to the AC bus. The ENAIR 70 wind turbine type and a diesel generator (Keno) used in the study are connected to the AC bus. A converter system is in between the AC and DC bus, while the photo voltaic (PV) solar panel and battery H1500 are connected to the DC bus respectively. A brief detailed presentation of the model system parameters is given in subsequent section of this chapter. Table 1 shows some of the merits and limitations of using the HOMER software [10].

 Merits Limitations Simulates a list of real technologies, as a catalogue of available technologies and components Quality input data needed (sources) Very detailed results for analysis and evaluation. Detailed input data (and time) needed Determines the possible combinations of a list of different technologies and its size. An experienced criterion is needed to converge to the good solutions It is fast to run many combinations. HOMER will not guess key values or sizes if there are missed. Results could be helpful to learn a system configuration and optimization. Could be time consuming and onerous

### Table 1.

Merits and limitations of HOMER

## 3. Inputs and assumptions of system model

The load profile used for this study is shown in Figure 2, where there was a peak of 8.2kW at the early hours of the morning and dropped to 4kW until around 6am, where a slight peak of above 6kW was observed. Just before noon and after noon, the load profile slightly increased and decreased respectively and a gradual peak was observed in the evening between 6pm and about 11pm going as high as 13kW. A scaled annual average of 160, 147kWh/d, scaled peak load of 12.8, 11.7kW and a load factor of 0.522 was considered in this study as shown in Table 2.

### Table 2.

PV

The detail of the PV system is shown in Table 3. A 20 year lifetime and derating factor of 80% were considered. The slopes were 14, 24 degs, with a ground reflectance of 20%.

### Table 3.

PV Parameters

Solarresource

Table 4 shows the parameters of the solar resource, where the maximum average radiation occurred in the month of April. The scaled annual average is 6.04kWh/m2/day.

### Table 4.

Solar Resource Parameters

The daily radiation and clearness index of the solar resource is shown in Figure 4.

ACwind turbine:ENAIR70

ENAIR wind turbine with different quantities and hub height of 15m was used in this study. The details and power curve of the wind turbine are shown in Table 5 and Figure 4 respectively.

### Table 5.

Details AC Wind Turbine

Windresource

The wind resource data used for this study is shown in Tables 6 and 7, with the peak wind speed occurring in January, while the least wind speed in August, while a plot of the wind speed for the various months is shown in Figure 5.

### Table 6.

Wind speed distribution

### Table 7.

Details of Wind Resource

ACgenerator:Keno

The details of the AC generator details are given in Table 8, while its efficiency is shown in the simulation results.

### Table 8.

Details of AC Generator

Fuel:Diesel

The fuel details are shown in Table 9.

### Table 9.

Details Fuel Type

Battery:Hoppecke12OPzS 1500

The Hoppecke 12 OPzS 1500 battery parameters used are shown in Table 10. A battery spring of 24 was considered in the study.

### Table 10.

Battery Parameters

Converter

Table 11 shows the parameters of the converter system.

### Table 11.

Converter Parameters

## 4. Grid extension/ economics/generator control

A grid extension was compared to stand alone system to know if it is cheaper to use the grid or the stand alone system. Details of the grid extension, economics of the system and generator control are shown in Table 12.

### Table 12.

Grid Extension/Economics/Generator Control Parameters

## 5. Emissions/Constraints

The emissions and constraints in running the system are described in Table 13. It would be discovered that the emissions are zero due to the renewable energy level of operation.

### Table 13.

Emissions and Constraints of the System

## 6. Simulation results and analysis

Simulations were run in the HOMER software for various configuration system types of the power system, in order to obtain the most efficient system configuration that would give the lowest net present cost to determine the basis of energy efficiency. Figure 6 shows all the possible configurations and results that can be obtained using the various power sources.

## 9. Conclusion

The use of hybrid optimization model for electrical renewable (HOMER) software has been presented in this chapter for design of a power system mainly composed of electric renewables. Hybrid Optimization Model for Electrical Renewable (HOMER), is a micro power optimization model, that simplifies the task of evaluating designs of both off-grid and grid-connected power systems for a variety of applications. The HOMER Hybrid Optimization Modeling Software is used for designing and analyzing hybrid power systems, which contain a mix of conventional generators, cogeneration, wind turbines, solar photovoltaic, batteries, fuel cells and other inputs. In order to determine the optimized system configuration that would be more energy efficient, the net present cost was used as the basis for the selection of the best operation conditions considering a system made up of a PV, wind turbine, AC diesel generator, battery and converter systems. The lowest net present cost of the various solutions was chosen as the optimized configuration.

Also, HOMER would give idea of the best rating of the PV, the number of wind turbines, the rating of the AC diesel generator, number of battery springs, rating of the converter system, initial cost, operating cost, total net present cost, cost of energy per kWh, renewable energy fraction, capacity storage, diesel consumption in liters, and the generator hours of operation. HOMER contains a powerful optimizing function that is useful in determining the cost of the various energy project scenarios as shown in the text of this chapter. This functionality allows for minimization of cost and optimization of scenarios based on various factors.

Furthermore, a model system consisting of wind turbine, PV system, diesel ac generator, battery and converter system was investigated using different load profiles. The cash flow summary results demonstrates that increase load profile leads to more capital, operating, replacement, increase fuel, and salvage value of the project for the wind turbine, PV, diesel and battery systems. However, the converter system was found to be independent of the load profiles.

## References

1. 1. National Renewable Energy Laboratory. Energy Efficiency and Renewable Energy, USA, 2008.
2. 2. ECOWAS Center for Renewable Energy and Energy Efficiency (ECREEE). HOMER Software for Renewable Energy Design, 2013.
3. 3. ESMAP Technical Paper 121/07 Technical and Economic Assessment of Off-grid,Mini-grid and Grid Electrification Technologies. The World Bank, Washington, 2007. http://www.ecowrex.org/document/technical-and-economic-assessment-grid-mini-grid-and-grid-electrification-technologies.
4. 4. Givler T, and Lilienthal P. Using HOMER Software, NREL’s Micro power Optimization Model, to explore the Role of Gen-sets in Small Solar Power Systems; Case Study: Sri Lanka Technical Report. National Renewable Energy Laboratory, USA, 2005.
5. 5. Kassam A. HOMER Software Training Guide for Renewable Energy Station Base Design. Green Power for Mobile. 2010.
6. 6. Alabdul Salam M. et al. Optimal sizing of photovoltaic systems using HOMER for Sohar, Oman. International Journal of Renewable Energy Research. 2013; 3(2):301 – 307.
7. 7. Al-Karaghouli A., Kazmerski L.L Optimization and Life-Cycle Cost of Health Clinic PV System for a Rural Area in Southern Iraq using HOMER Software. Solar Energy. 2010; 84: 710-714.
8. 8. Ajao K. R., Oladosu O.A and Popoola O.T. Using HOMER Power Optimization Software for Cost Benefit Analysis of Hybrid-Solar Power Generation Relative to Utility Cost in Nigeria. IJRRAS. 2011; 7(1): 96-102.
9. 9. Ahmed S., Hasnaoui Othman, Sallami Anis. Optimal Sizing of a Hybrid System of Renewable Energy for a Reliable Load Supply without Interruption. European Journal of Scientific Research. 2010; 45(4): 620-629.
10. 10. Okedu K.E and Roland Uhunmwangho. Optimization of Renewable Energy Efficiency using HOMER. International Journal of Renewable Energy Research. 2014; 4(2): 421-427.
11. 11. Ahmed S., Hasnaoui Othman, Sallami Anis. Optimal Sizing of a Hybrid System of Renewable Energy for a Reliable Load Supply without Interruption. European Journal of Scientific Research. 2010; 45(4): 620-629.
12. 12. Fraunhofer Institute for Solar Energy System (ISE). Levelized Cost of Electricity Renewable Energy Technologies. November 2013.
13. 13. Ueckerdt F., Hirth L., Luderer G., and Edenhofer O. System LCOE: What are the Costs of Variable Renewables. Postdam – Institute for Climate Impact Research. Germany. 2013.
14. 14. Ocampo M. T. How to Calculate the Levelized Cost of Energy-a Simplified Approach. Energy Technology Expert. 2009.
15. 15. AboGaleela M., El-Marsafaway M., and El-Sobki M. Optimal Scheme with Load Forecasting for DSM in Residential Areas. Energy and Power Engineering. 2013; 5: 889-896.
16. 16. Gellings C.W, Smith M.W. Integrating Demand Side Management into Utility Planning. Proceedings of the IEEE. 1989; 77(6); 908-918.

Written By

Kenneth E. Okedu, Roland Uhunmwangho, Ngang Bassey Ngang and Richard Azubuike John

Submitted: June 18th, 2014 Reviewed: August 25th, 2014 Published: April 22nd, 2015