ABSTRACT
This study presents the modelling and optimization of a Hybrid Energy System (HES) for GSM Base Transceiver Station (BTS) sites in emerging cities. The aim is to ensure reliable and cost-effective power supply, considering the availability, dynamism and viability of energy sources.
Theoretical approach is applied in the modelling, simulation and validation of the developed HES, which consists of the utility grid, wind and solar photovoltaic (PV) as primary energy sources incorporating a super-capacitor/battery storage and power conversion unit. The complexity in optimizing continuous variables of the HES informed the use of a hybrid Genetic Algorithm and Pattern Search (h-GAPS) technique. The optimization problem is treated as a single objective function by considering all objectives in terms of cost while constraining the HES to satisfy the load demand safely according to the reliability criteria defined by the energy management strategy. The h-GAPS based optimization model simulated for the peripheral node GSM BTS sites in Abuja, Benin City, Enugu, Ikeja, Maiduguri and Sokoto utilized long-term (22-years) meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration. The performance index of various developed and existing energy systems is evaluated based on economy or Cost of Energy (COE), power system reliability, energy throughput, and emission reduction targets.
Simulation results showed that Sokoto is the most favourable site for utilizing the proposed HES. Abuja and Benin City are the least favourable locations for utilizing the grid-connected (Grid/PV/Wind) and the off-grid (PV/Wind) configurations respectively. The optimum size
of grid-connected HES consisting of 2 kW wind turbine, 7.09 m2 PV array inclined at 150,
0.053 kWh super-capacitor and 10.8 kWh (48V, 225Ah) battery banks, and 1,484.60 kWh of energy drawn from the grid per annum enabled reliable (negligible power loss) and cost- effective energy supply in Sokoto. The off-grid configuration reduced the COE by 72.81% (N24.75 to N6.73 per kWh) but with larger PV array size (12.68 m2) and reliability of 99.02%. In comparison with current practice of using grid/diesel systems, the proposed off- grid configuration has the best performance index, with an average energy throughput of
0.076 kWh per naira, in Nigeria. A reliable and cost effective energy option will not only reduce the per-unit cost of mobile services in Nigeria, but also reduce the greenhouse gas emission level from GSM BTS sites by an average of 98.34% thereby making the environment much more friendly and safe. This research would be useful for mobile service providers, consultants, regulatory agencies, policy makers, and the society.
TABLE OF CONTENTS
TITLE PAGE i
CERTIFICATION OF THESIS ON PLAGIARISM iii
CHAPTER ONE: GENERAL INTRODUCTION 1
CHAPTER TWO: LITERATURE REVIEW 15
- Overview of GSM Base Transceiver Station Sites 15
| 2.7.1 | Sokoto | 101 |
| 2.7.2 | Maiduguri | 102 |
| 2.7.3 | Abuja | 102 |
| 2.7.4 | Ikeja | 103 |
| 2.7.5 | Enugu | 103 |
| 2.7.6 | Benin City | 104 |
- Summary 104
CHAPTER THREE: METHODOLOGY 108
- Development of the Hybrid Energy System Model 108
- Modelling of the Hybrid Energy System Components 111
- Modelling of the Grid Energy Supply System (GESS) 111
- Modelling of the Wind Energy Conversion System 114
- Modelling of the Hybrid Energy System Components 111
- Modelling of the Photovoltaic Conversion System 118
- Modelling of the Power Electronics (Conversion Unit) 123
- Modelling of the Energy Storage Unit 124
- Energy Management Strategy 129
- Control Design 129
- Operation Strategy 132
- System Energy Characteristics 134
- System Reliability Considerations 136
- System Techno-Economic Analysis 137
- Basic Considerations 138
- Economic Analysis 140
- Technical Analysis 146
- Total Cost Analysis 147
- Techno-Economic Viability 148
- Environmental Impact Assessment 148
- Estimation of Energy Consumption of GSM BTS Site 150
- Data Collection and Analysis 151
- Load Data 151
- Wind Turbine Characteristic Curve 152
- Meteorological Data 152
- Model Performance Evaluation 155
- Evaluation of various Global Solar Radiation Models for Nigeria 155
- Evaluation of various Diffuse Solar Radiation Models for Nigeria 158
- Determination of the Optimum Tilt Angle of a PV Array Oriented
Due South in Nigeria 159
| 3.8.4 Calibration and Validation of the Proposed Wind Energy Conversion System Model 160 Optimization Procedure 161 3.9.1 Formulation of the Optimization Problem 161 3.9.2 Optimization of the Proposed Hybrid Energy System 163 Design of Simulation Model 168 3.10.1 Case Studies: Process Simulation and Application 169 |
3.9
- 3.10
CHAPTER FOUR: RESULTS, DISCUSSION AND FINDINGS 173
GSM BTS Sites 211
| 4.4.1 | Comparison of Proposed Energy System and Existing Energy Systems | 214 | |
| 4.4.2 | System Environmental Impact | 227 | |
| 4.4.3 | Land Requirement for Implementing the Proposed Hybrid Energy System for GSM BTS Sites | 229 | |
| 4.5 | Findings | 230 |
4.6 Contributions to Knowledge 234
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS 235
- Further Research 238
LIST OF RESEARCH PUBLICATIONS 240
REFERENCES 242
APPENDICES 280
Appendix A: Main Technical Specifications of Hybrid Energy System Components 280
Appendix B: Meteorological and Load Data 285
Appendix C: MATLAB Script for Model Calibration and Validation 297
Appendix D: MATLAB Script for Determining the Optimum Tilt Angles of PV Array 305 Appendix E: MATLAB Main Scripts for Optimization and Operation Control of Proposed
Hybrid Energy System 305
LIST OF FIGURES
Figure 1.1: Trends of subscribers‘ base and teledensity in the period (2001 – September 2014) in Nigeria (NCC, 2014). 2
Figure 1.2: Market shares of GSM network operators as in September 2014 in Nigeria (NCC, 2014). 2
Figure 2.1: Funding of the Nigerian power sector in the last three decades (Tallapragada, 2009; Erik, 2011) 19
Figure 2.2: Trends of the Nigerian population within the last three decades (1980 – 2011) 20
Figure 2.3: Average duration of power access and outage of the Nigerian grid electricity system (UNDP-GEF, 2013) 23
Figure 2.4: Average duration of power access per day of the Nigerian grid electricity system (UNDP-GEF, 2013) 23
Figure 2.5: Variation of cost of grid-supplied electricity to a typical GSM BTS site for different locations across Nigeria. 28
Figure 2.6: Share of different anthropogenic GHG emissions, in total emissions in 2004, expressed in terms of CO2-eq (IPCC, 2007). 29
Figure 2.7: Comparison of the cubic law fitted WTG characteristics and that of the typical WTG supplied by the manufacturer for Hummer H3.1-1kW WTG (AHDC, 2013) 44
Figure 2.8: Effect of varying altitude on air density (Okundamiya and Nzeako, 2013) 45
Figure 2.9: Map of Nigeria showing the study locations. 101
Figure 3.1: Architecture of the proposed hybrid energy system for electricity supply to GSM BTS sites 108
Figure 3.2: Control design of proposed hybrid energy system 130
Figure 3.3: Control design of proposed micro-grid (stand-alone HES) 132
Figure 3.4: Optimization and operation control model for HES 165
Figure 3.5: GA toolbox parameter settings for the simulation model 168
Figure 4.1: Simulated minute load profile for an outdoor GSM BTS (S/4/4/4) site in Nigeria. 173
Figure 4.2: The Nigerian grid supply voltage profile simulated for a period of 1 year
- Voltage magnitude (b) Power access/outage frequency. 174
Figure 4.3: Comparison of the measured and estimated monthly average daily global solar radiations on a horizontal surface (in the periods of 1984 – 2005) for
(a) Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja, (e) Enugu, and
(f) Benin City respectively 177
Figure 4.4: Comparison of performance indices for various global solar radiation models: (a) r-value, (b) RMSE, (c) MBE and (d) MABE. 178
Figure 4.5: Comparison of the monthly relative percentage error (RPE) of global solar radiation estimates from (a) Angstrom–Prescott (1940), (b) Badescu (1999), (c) Chen et al. (2004), (d) El-Metwally (2004), (e) Falayi et al. (2008), and (f) Present study [Eq. (3.22)]. 179
Figure 4.6: Comparison of the measured and estimated monthly average daily diffuse solar radiations (1984–2005) for (a) Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja, (e) Enugu, and (f) Benin City respectively 181
Figure 4.7: Comparison of performance indices for various diffuse solar radiation models: (a) r-value, (b) RMSE, (c) MBE and (d) MABE. 183
Figure 4.8: A comparison of the monthly relative percentage error (RPE) of diffuse solar radiation estimates from (a) Page (1964), (b) Liu-Jordan (1960), (c)
Butt et al. (2010), (d) Karakoti et al. (2011), (e) Present study (Eq. 3.23),
and (f) Present study (Eq. 3.24). 184
Figure 4.9: Comparison of annual total solar irradiance on a horizontal and tilted surface
for various locations in Nigeria 189
Figure 4.10: A comparison of annual minutely global irradiance at annual optimum tilt angle for (a) Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja, (e) Enugu and
(f) Benin City. 190
Figure 4.11: Comparison of the proposed WTG power profile and the manufacturer supplied power characteristics for (a) H3.1-1kW, (b) H3.8-2kW, (c) H4.6- 3kW, and (d) H6.4-5kW WTGs respectively. 193
Figure 4.12: Adjusted minutely wind speed data to the GSM BTS tower height of 25 m for a typical year for (a) Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja,
(e) Enugu, and (f) Benin City respectively 194
Figure 4.13: Hourly energy profile for Grid-PV/Wind HES in Sokoto: (a) Total energy generation, (b) Energy drawn from grid, (c) Solar energy generation, and
(d) Wind energy generation. 199
Figure 4.14: SOC of SC/battery bank of proposed grid-PV/Wind energy system for a simulation period of one year for (a) Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja, (e) Enugu, and (f) Benin City respectively. 200
Figure 4.15: SOC of SC/battery bank of proposed stand-alone PV/Wind energy system for a simulation period of one year for (a) Sokoto, (b) Maiduguri,
- Abuja, (d) Ikeja, (e) Enugu, and (f) Benin City respectively. 201
Figure 4.16: Electrical characteristics of proposed stand-alone PV/Wind energy system for a typical day (February 28) for (a) Sokoto, (b) Maiduguri, (c) Abuja,
- Ikeja, (e) Enugu, and (f) Benin City respectively. 202
Figure 4.17: Electrical characteristics of proposed grid- PV/Wind HES for a typical day (September 21) for (a) Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja,
- Enugu, and (f) Benin City respectively. 203
Figure 4.18: Comparison of the power supply reliability of proposed hybrid and conventional energy systems 215
Figure 4.19: Comparison of the economic performance of proposed hybrid and conventional energy systems 218
Figure 4.20: Comparison of the performance viability of proposed and conventional energy systems 220
Figure 4.21: Variation of energy performance index with power supply reliability for
- Sokoto, (b) Maiduguri, (c) Abuja, (d) Ikeja, (e) Enugu, and (f) Benin City. 225
Figure 4.22: Comparison of the growth in cost saving and population till 2020 227
LIST OF TABLES
Table 2.1: The main characteristic of the Nigerian grid electricity system (UNDP-GEF, 2013). 24
Table 2.2: Variation of electricity tariff for mobile telecommunication companies for study locations (NERC, 2012) 27
Table 2.3: Global Warming Potential (IPCC, 1996) 30
Table 2.4: Weibull shape and scale factors for study locations in Nigeria (Ahmed et al., 2013) 35
Table 2.5: Power law exponents for different locations in Nigeria (Okundamiya and Nzeako, 2013) 41
Table 2.6: GHG emission factors for the grid electricity from various sources (Nandi
and Ghosh, 2010; LGOP, 2010) 80
Table 3.1: Geographical classification and coordinates of selected sites in Nigeria. 154
Table 3.2: Economic specifications of components for optimization of the proposed hybrid energy system (Nandi and Ghosh, 2010; AHDC, 2013; SEDC, 2013; MTI, 2014, Ebay, 2014) 171
Table 4.1: Calibration results of various global solar radiation models (using group-1
data sets for a period of 22-years) along with R2 and t-stat values 175
Table 4.2: Validation results of various global solar radiation models using group-2 data
sets for a period of 22-years 176
Table 4.3: Calibration results of different diffuse solar radiation models (using group-1
data sets for a period of 22-years) along with R2 and t-stat values 180
Table 4.4: Validation results of different diffuse solar radiation models using group-2
data sets for a period of 22-years 182
Table 4.5: Result of analysis (based on HDKR model) of influence of annual-based optimum tilt angles for selected areas in Nigeria 185
Table 4.6: Result of analysis (based on HDKR model) of influence of seasonal-based optimum tilt angles for selected locations in Nigeria 186
Table 4.7: Result of analysis (based on HDKR model) of influence of monthly-based optimum tilt angles for selected locations in Nigeria 187
Table 4.8: Comparison of different optimization methods (using HDKR model) for tracking solar radiations in Nigeria 188
Table 4.9: Calibration results of proposed WTG model [Eq. (3.19)] using manufacturer‘s supplied data. 191
Table 4.10: Validation results for proposed WTG model 192
Table 4.11: Simulation results for proposed Grid-PV/Wind hybrid energy system for considered locations 195
Table 4.12: Simulation results for proposed stand-alone PV/wind hybrid energy system
for studied locations 196
Table 4.13: The annual total energy composition of the developed Grid-PV/Wind HES 197
Table 4.14: The annual total energy composition of the developed stand-alone PV/Wind HES 198
Table 4.15: Comparison of Energy Composition of Grid-PV/Wind HES by fraction for different sites 216
Table 4.16: Comparison of Energy Composition of stand-alone PV/Wind HES by fraction for different sites 216
Table 4.17: Comparison of the costs of energy (COE) of various energy systems 217
Table 4.18: Comparison of proposed system techno-economic viability for various reliability limits 222
Table 4.19: Comparison of environmental impact of conventional and proposed energy systems 228
NOMENCLATURES
| ∆T | Temperature difference (oC) |
| A | Area (m2) |
| apm | Maximum coefficient of rated performance |
| apr | Coefficient of performance at rated wind speed |
| B | Temperature lapse rate (K m-1) |
| bi | Model parameters/regression coefficients |
| Cˆ | Monthly average daily total Cloud cover during daytime observations (octa) |
| c | Present cost penalty per unit size ( |
| cj | Present cost of component j per unit size ( |
| ccj | Cost coefficient of component j per unit size ( |
| CO2–eq | Carbon footprint (t) |
| D, G | Coefficients |
| E | Total energy (kWh yr-1) |
| E (τ) | Energy at time τ (kWh) |
| ef, | Emission factors (g kWh-1) |
em, Emission (t)
| fm |
~ Modulating function
- Gravitational acceleration (m s-2)
H Monthly average daily global radiation on a horizontal surface (kWh m-2 day-1)
- Hour (h)
Hc Monthly average clear sky daily global radiation on a horizontal surface (kWh m-2 day-1)
HD Monthly average daily diffuse radiation on a horizontal surface (kWh m-2 day-1)
Ho Monthly average daily extraterrestrial radiation on a horizontal surface (kWh m-2 day-1)
I Average hourly global solar radiation on a horizontal surface (kW m-2)
I0 Hourly extraterrestrial radiation on a horizontal surface (kW m-2)
Ib Average hourly beam solar radiation on a horizontal surface (kW m-2) Id Average hourly diffuse solar radiation on a horizontal surface (kW m-2) It Hourly global irradiance incident on a tilted surface (kW m-2)
k Shape factor
K ¢ Monthly average daily diffuse coefficient
| KD | Monthly average daily diffuse fraction |
| KT | Monthly average daily clear index |
| L | Lifetime (yr) |
| l | Scale factor (m s-1) |
| m | Months of the year |
| min | Minutes |
| N | Number |
| nf | Noise factor |
| ns | Outcomes of supply |
| ol | Moving average function at lag l |
| P | Power (kW) |
| Probability density functions | |
| Ps | Pressure (Pa) |
| Py | Probability |
| Q | Quantity |
| r | Coefficient of correlation |
| R | Gas Constant (J kg-1 K-1) |
R2 Coefficient of determination
| rd ȓe Rg¢ | Self-discharge rate (% day-1) Escalation rate Geometric ratio |
| RH | Monthly average daily relative humidity (%) |
| ȓi | Interest rate |
| ȓf | Inflation rate |
| S | Monthly average sunshine duration (h) |
| So | Monthly average daylight sunshine duration (h) |
| Sz | Size |
| T | Air temperature (oC) |
| T* | Absolute temperature (K) |
| tcp | Temperature coefficient of power (% 0C -1) |
| t-stat | T-statistic test |
| uh | Coefficient of heat transfer/loss to surroundings (kW m-2 °C -1) |
| ul | Autocorrelation function at lag l |
| v | Wind speed (m s-1) |
v Standardized wind speed (m s-1)
V Voltage (V)
Xp Expectation
yr Year
z Altitude (m)
Z , B
Matrix
Greek letters
µ Average
ħf Horizon brightening factor
ƛi Anisotropy index
α Solar absorbance
β Surface inclination or tilt angle (o)
γ Azimuth or surface orientation (o)
δ Solar declination (o)
ε Noise function
η Efficiency
θz Zenith angle of incidence (o)
λ Power law exponent
ξ Solar transmittance of any cover over the PV array
ρ Air density (kg m-3)
ρg Ground reflectance or Albedo
σ Standard deviation
σ2 Variance
τ Simulation time (s)
ϕ Latitude (o)
ψη Wavelet function
ω Hour angle (o)
ωs Sunset Hour Angle (o)
Subscripts
ac Alternating current
ah Anemometer height
ann Annual
ave Average
bat Battery
| bb | Battery bank |
| bf | Battery float-life |
| cf | Coupling factor |
| ci | Cut-in |
| cl | Cell |
| co | Cut-out |
| con | Converter |
| cr | Critical |
| d | Demand |
| dc | Direct current |
| def | Deficit |
| ec | Economic |
| en | Environmental |
| es | Energy storage |
| exc | Excess |
| fc | Fixed charge |
| ga | Grid access |
| gc | Grid consumption |
| ge | Grid electricity |
| gec | Grid energy contribution |
| grd | Grid |
| gs | Grid supply |
| hh | Hub height |
| hor | Horizontal |
| hs | Hybrid system supply |
| ic | Input of converter |
| inc | Inclination |
| ini | Initial |
| int | Interconnection |
| inv | Inverter |
| lt | Lifetime |
| max | Maximum |
| mea | Measured |
| min | Minimum |
| mo | Mode of operation |
| mod | PV Module |
| mp | Maximum power |
| norm | Normalized |
| op | Operation |
| out | Output |
| p | Power |
| pk | Peak |
| pred | Predicted |
| R | Ratio/Relative |
| rat | Rated |
| re | Renewable energy |
| rec | Renewable energy contribution |
| rel | Reliability |
| rep | Replacement |
| ret | Rectifier |
| sal | Salvage |
| sc | SC bank |
| se | Solar energy |
| sg | Solar generation |
| sta | Stabilizer |
| STC | Standard Test Conditions |
| sv | Salvage value |
| sys | System |
| te | Technical |
| tec | Total energy contribution |
| tp | Throughput |
| un | Unmet |
| we | Wind energy |
| wec | Wind energy contribution |
| wg | Wind generation |
| wt | Wind turbine |
| Acronyms | |
| 3G | Third Generations |
| AC | Alternate Current |
| ACO | Ant Colony Optimization |
| ANN | Artificial Neural Network |
| AR | Autoregressive |
| ARMA | Autoregressive Moving Average |
| BTS | Base Transceiver Station |
| CER | Commission for Energy Regulation |
| COE | Costs of Energy |
| COI | Cost of Investment |
| CRF | Capital Recovery Factor |
| D | Decision variable |
| DC | Direct Current |
| DE | Differential Evolution |
| DML | Digital Mobile Licenses |
| DOD | Depth of Discharge |
ECN Energy Commission of Nigeria
EPSR Electricity Power Sector Reform
erf Error function
ES Energy Storage
