Homer Thesis Brasil Analysis of Regional Energy Supply Systems 01

University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Analysis of Regional Energy Supply Systems

by

Everson Possamai Date and Place of Birth: 5 February 1971 in Bento Goncalves / Brazil

Master Thesis in the Postgraduate Programme

RENEWABLE ENERGY

Energy and Semiconductor Research Institute of Physics Faculty of Mathematics & Science Carl von Ossietzky University Oldenburg / F. R. Germany Day of Examination: 1. Examiner: 2. Examiner:

Everson Possamai, Sep/2004

6th September 2004 Dr. D. Heinemann Priv.-Doz. Dr. H. G. Beyer

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Acknowledgments

I want to thank God for giving me a wonderful family, partner of all the effort done, and the opportunity to learn and grow every day.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

DECLARATION I state and declare that I have prepared this thesis "Strategy for Analysis of Regional Energy Supply Systems" by myself using only means and sources as cited.

Oldenburg, 6th September, 2004 _________________________________ (Everson Possamai)

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

1 Abstract In this thesis, a strategy for decision-making is proposed, based on evaluation tools easy to obtain and operate. The options analysed and compared among themselves are: home systems (PV and Wind), Diesel mini-grid and grid extension in a unique location as case study for demonstration. The strategy can be used afterwards for any case and location with the same or other energy supplying options, serving as a useful tool for decision of local committees, utilities and subcontractors implementing the Light for All (Luz para Todos) governmental program for electricity access of all citizens in Brazil. The three options are compared according to their NPC (Net Present Cost), also referred as lifecycle cost.

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Table of Contents 1 2 3 4

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Abstract......................................................................................................... 4 The Light for All program .......................................................................... 10 2.1 Social Aspects and Funding................................................................ 10 2.2 Technical and Economical Aspects .................................................... 12 Evaluation Strategy..................................................................................... 13 The softwares used...................................................................................... 15 4.1 Homer ................................................................................................. 15 4.1.1 Features........................................................................................... 15 4.1.1.1 Simulation................................................................................ 16 4.1.1.2 Optimisation............................................................................. 17 4.1.1.3 Sensitivity Analysis ................................................................. 17 4.1.1.4 Load inputs .............................................................................. 17 4.1.2 PV panels inputs ............................................................................. 20 4.1.3 Wind turbine inputs ........................................................................ 20 4.1.4 Diesel gen-set inputs....................................................................... 21 4.1.5 Battery inputs.................................................................................. 22 4.1.6 Converter inputs.............................................................................. 25 4.1.7 Solar and Wind resource inputs ...................................................... 26 4.1.8 Economic and Constraint inputs ..................................................... 28 4.1.9 Results............................................................................................. 28 4.2 Radiasol .............................................................................................. 29 4.3 Sizing 5 ............................................................................................... 29 4.4 DICcalc ............................................................................................... 34 Economic analysis of costs: the theory behind Homer............................... 35 5.1 Interest Rates and Inflation ................................................................. 35 5.1.1 Nominal Interest Rate ..................................................................... 35 5.1.2 Inflation........................................................................................... 35 5.2 Operations with Values....................................................................... 36 5.2.1 Future Value (FV) of a Present Value (PV) after n times .............. 37 5.2.1.1 Example a ................................................................................ 37 5.2.1.2 Example b ................................................................................ 37 5.2.2 Present Value (PV) of a known value in the time n........................ 37 5.2.2.1 Example a ................................................................................ 38 5.2.2.2 Example b ................................................................................ 38 5.2.3 Present Value (PV) of an increasing series and the opposite ......... 39 5.2.3.1 Example ................................................................................... 40 5.2.4 Present Value of an annuity (constant value) and the opposite ...... 40 5.2.4.1 Example ................................................................................... 40 5.3 Summary of rates ................................................................................ 41 Case study ................................................................................................... 41 6.1 Site selection ....................................................................................... 41 6.2 Solar resource input ............................................................................ 46 6.3 Wind resource input............................................................................ 46 6.4 Load input for a single household....................................................... 48 6.5 Load input for a village....................................................................... 51 6.6 Economics and Constraints inputs...................................................... 51 6.7 Components: characteristics and costs ............................................... 52

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

6.7.1 PV generator ................................................................................... 52 6.7.2 Battery............................................................................................. 53 6.7.3 Converter ........................................................................................ 55 6.7.4 Wind turbine ................................................................................... 55 6.7.5 Grid ................................................................................................. 57 6.7.6 Diesel mini-grid .............................................................................. 62 6.8 Analysis of options for a single household......................................... 65 6.8.1 Solar Home Systems....................................................................... 65 6.8.1.1 Design results summary........................................................... 70 6.8.2 Wind Home Systems ...................................................................... 70 6.8.2.1 Design results summary........................................................... 73 6.8.3 Evaluation of the best option for a single household...................... 73 6.9 Analysis of options for a community.................................................. 75 6.9.1 Diesel mini-grid .............................................................................. 75 6.9.1.1 Minimum load study................................................................ 76 6.9.1.2 Load per line length and fuel cost study .................................. 76 6.9.2 Several Home Systems ................................................................... 78 6.9.3 Evaluation of the best option for a community............................... 78 7 Conclusions about the proposed strategy.................................................... 80 8 References................................................................................................... 81 9 Appendices.................................................................................................. 83 9.1 Interest rates paid by bank investments .............................................. 84 9.2 Names and addresses of the participant companies............................ 85 9.3 CV of the author ................................................................................. 86 9.4 Final Mark and Certificate.................................................................. 89

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LIST OF TABLES Table 1: Classification and supplying characteristics [7]. .............................................. 12 Table 2: Official inflation index of the last 4 years in Brazil (source: Banco Central do Brasil)...................................................................................................................... 36 Table 3: Access of potable water and electricity in Sao Jose do Norte. ......................... 45 Table 4: Population and electricity access data of Sao Jose do Norte ............................ 46 Table 5: Global horizontal radiation (kWh/m² day) assumed for Sao Jose do Norte..... 46 Table 6: Case study load summary. ................................................................................ 49

LIST OF FIGURES Figure 1: Electrification scenario and its similarity to the human development scenario [4]............................................................................................................................ 10 Figure 2: The share of funding [2], [3]. .......................................................................... 11 Figure 3: Logic of the evaluation strategy. ..................................................................... 14 Figure 4: Homer first screen with indication of main fields........................................... 16 Figure 5: Homer load inputs screen................................................................................ 18 Figure 6: Load as it is entered in the Baseline................................................................ 19 Figure 7: Load after daily noise is added........................................................................ 19 Figure 8: Load after daily and hourly noise are added. .................................................. 19 Figure 9: Homer PV inputs screen.................................................................................. 20 Figure 10: Homer wind turbine inputs screen. ............................................................... 21 Figure 11: Homer generator inputs screen...................................................................... 22 Figure 12: Homer battery inputs screen.......................................................................... 23 Figure 13: Homer converter inputs screen...................................................................... 25 Figure 14: Homer solar resource inputs screen. ............................................................. 26 Figure 15: Homer wind resource inputs screen. ............................................................. 27 Figure 16: Homer typical presentation of results............................................................ 28 Figure 17: Radiasol main screen..................................................................................... 29 Figure 18: Sizing 5 loads input screen example (fill in the green cells)......................... 30 Figure 19: Sizing 5 presentation of the load profile in a typical day.............................. 31 Figure 20: Sizing 5 information of the share of each load in the daily load profile. ...... 31 Figure 21: Sizing 5 load summary presentation. ............................................................ 31 Figure 22: Sizing 5 design screen. .................................................................................. 33 Figure 23: DICcalc results screen................................................................................... 34 Figure 24: Conversion from PV to FV. .......................................................................... 37 Figure 25: Conversion from FV to PV. .......................................................................... 37 Figure 26: Conversion of PV to FV in the example. ...................................................... 38 Figure 27: Conversion of FV to PV in the example. ...................................................... 38 Figure 28: Placing one value in the timeline without inflation and calculating its PV. . 39 Figure 29: Operations with series of values.................................................................... 39 Figure 30: Annuity.......................................................................................................... 40 Figure 31: Number of households without electricity access per state of the federation, 2003 [5]................................................................................................................... 42 Figure 33: HDI and electricity access index in Rio Grande do Sul [4]. ......................... 43 Figure 34: Annual average wind speed at 50m height (m/s) [8]. ................................... 44 Everson Possamai, Sep/2004

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 35: Annual average solar radiation on horizontal surface [9] ............................. 44 Figure 36: Position of Sao Jose do Norte in the map of Rio Grande do Sul. ................. 45 Figure 37: Correction of the wind speed due to height................................................... 47 Figure 38: Panoramic view of Sao Jose do Norte and rugosity map [8]. ....................... 47 Figure 39: Daily wind speed profile for every month in the vicinity of Sao Jose do Norte [8]............................................................................................................................ 48 Figure 40: Case study load profile of every representative day of the year. .................. 49 Figure 41: Case study load share of the appliances. ....................................................... 50 Figure 42: Characteristics, costs and Homer inputs of the PV modules......................... 52 Figure 43: Characteristics, costs and Homer inputs of the battery. ................................ 54 Figure 44: Characteristics, costs and Homer inputs of the converter. ............................ 55 Figure 45: Characteristics, costs and Homer inputs of the wind turbines. ..................... 56 Figure 46: Diagram of grid extension............................................................................. 57 Figure 47: Capital cost of grid extension as function of the community size and line length [17]............................................................................................................... 58 Figure 48: Capital cost of grid extension as function of the community size and line length (zoom) [17]. ................................................................................................. 58 Figure 49: Grid extension cost for a single household with costs table. ....................... 60 Figure 50: Grid extension cost for 30 household with costs table.................................. 61 Figure 51: Generator cost as function of the nominal power (whole range of the study). ................................................................................................................................ 62 Figure 52: Generator cost as function of the nominal power (for the range of interest).62 Figure 53: Diesel mini-grid costs.................................................................................... 64 Figure 54: Diesel mini-grid Homer inputs...................................................................... 65 Figure 55: Frequency of the battery bank SOC. ............................................................. 66 Figure 56: Battery SOC hourly plot over the whole year. .............................................. 66 Figure 57: Plot of the main SHS operational parameters in a good and bad performance week. ....................................................................................................................... 67 Figure 58: DICcalc result for DIC evaluation of the SHS. ............................................. 68 Figure 59: Case study SHS costs with components share. ............................................. 68 Figure 60: Screen of Sizing 5 for the case study design................................................. 69 Figure 61: Plot of the main WHS operational parameters in a good and bad performance week. ....................................................................................................................... 71 Figure 62: Battery SOC hourly plot over the whole year. .............................................. 71 Figure 63: DICcalc result for DIC evaluation of the WHS. ........................................... 72 Figure 64: Case study WHS costs with components share............................................. 72 Figure 65: Graphic with costs of all electrification options for a single household in the case study................................................................................................................ 74 Figure 66: Diesel mini-grid performance in a typical day.............................................. 75 Figure 67: Effect of the minimum load parameter on the Diesel mini-grid NPC. ......... 76 Figure 68: Graphic with both fuel price and load/length effects on the Diesel mini-grid NPC......................................................................................................................... 77 Figure 69: Diesel mini-grid costs and cost categories share........................................... 77 Figure 70: Graphic with costs of all electrification options for the 30 households in the case study (NPC as function of the line length)...................................................... 78 Figure 71: Graphic with costs of all electrification options for the 30 households in the case study (NPC as function of the load/length)..................................................... 79

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List of Abbreviations AC ANEEL BCC CDE CGEU CGNU CNU COE DC DIC DOD HDI i IGPM LV MV NPC O&M PV RGR SHS SIGFI SOC WHS

Alternate Current Agencia Nacional de Energia Eletrica (Electric Energy National Agency) Battery Charge Controller Conta de Desenvolvimento Energetico (Energy Development Count) Universalization State Management Committee Universalization National Management Committee Universalization National Council Cost of Energy Continuous Current Duracao da Interrupcao do fornecimento de energia for unidade Consumidora (Energy Supplying Interruption per Consumer) Depth of Discharge Human Development Index Interest Rate Indice Geral de Precos no Mercado (Market Prices General Index) Low Voltage Medium Voltage Net Present Cost Operation and Maintenance Photovoltaic Reserva Geral de Reversao (Reversion General Reserve) Solar Home Systems Sistemas Individuais de Geracao por Fontes Intermitentes (Individual Energy Generation Systems Using Intermittent Sources) State of Charge Wind Home Systems

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2 The Light for All program 2.1 Social Aspects and Funding The Federal Government of Brazil through a Presidential Decree created the Light for All program in 11th November 2003 [1]. This program is also called National Program for Universal Access to the Electric Energy. Its goal is to bring, until 2008, electricity to the 12 million inhabitants that do not have access to this service, being 10 million in the rural areas. This amount of people corresponds to 6.7% of the total Brazilian population. Official statistics [4] reveal that the rural inhabitants, per region of the federation, without access to the electricity services are as follows: North: 62.5% (~ 2.6 million people) Northeast: 39.3% (~ 5.8 million people) Centre-West: 27.6% (~ 0.367 million people) Southeast: 11.9% (~ 0.807 million people) South: 8.2% (~ 0.484 million people) The electric exclusion is higher in the areas with lower Human Development Index (UN – HDI) and low wage income: - 84% of the families without electricity live in municipalities with HDI lower than the Brazilian average of 0.766 (other references: Germany 0.925; poorest countries around the world 0.323) - 90% of the families have wage bellow 3 Minimum Salaries1. Electrification

HDI

17% to 77%

0.467 to 0.672

85% to 93%

0.721 to 0.766

99% to 100%

0.780 to 0.919

Figure 1: Electrification scenario and its similarity to the human development scenario [4].

1

The Minimum Salary is a number defined by the Government which is supposed to reflect the minimum wage of a person in order to have his basic needs satisfied per month (the calculation used the Minimum Salary of the year 2000).

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With these social characteristics, an effort in electricity access to those without it can be also understood as an investment in life quality standard improvement and thus justify the application of public funds. Costing of R$ 7 billion (currently EU 1.89 billion), the program will be implemented in partnership between the state governments, electricity utilities and the society. They are organized in 3 implementation levels as follows: 1) Universalization National Council (CNU): coordinate the actions between the involved governmental bodies taking in account the governmental policies. Is headed by the Ministry of Mines and Energy and composed of governmental bodies (represented by their Ministers or Presidents) from all involved sectors. 2) Universalization National Management Committee (CGNU): its role is to coordinate, check the actions being done in the whole country. Is formed by representatives of the Ministry or Mines and Energy, energy generation and distribution, energy regulation, electrification cooperatives, State Energy Secretaries and by the regional coordinators of the program. 3) Universalization State Management Committee (CGEU): locally coordinate the activities and checks if the universalization goals are being reached. Is formed by representatives of the Ministry of Mines and Energy, state energy regulatory bodies, energy distributors and representatives of the civil society. The federal financial resources will come from national funds (CDE and RGR, see reference [5]) that have been fed with taxes paid by every energy consumer (commercial, industrial and residential) performing an amount of R$ 5.3 billion (EU 1.43 billion). The other part will be shared between the state governments and the utilities (see Figure 2). These last ones are already obliged by law to supply electricity to all interested inhabitants until 2015. With the federal and state government resources support plus the goals well defined the program wants to reduce this time until 2008 [3]. Utilities and Cooperatives 14% State and Municipal Governments 76%

Federal Government 76%

Figure 2: The share of funding [2], [3].

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

2.2 Technical and Economical Aspects The program accepts projects of grid extension, decentralized energy supply (mini-grid) and home systems. For mini-grids the NPC will be compared to grid extension for the sake of approval. In other words, mini-grids will be checked in cost against grid extension. The reference data for grid extension are those in the Eletrobras files. In the case of home systems, the project submitted to the CGEU has to show a cost comparison to other options (grid extension mainly). Again, this indicates that the least cost option has to be considered by the executor agent. In the implementation, priority will be given to the use of local labour force and national equipment that, where possible, are manufactured close to the attended communities. In order to encourage renewable energy based systems, the ANNEL (National Agency for Electrical Energy) has proposed a regulation with minimum sizes, quality and consume reading characteristics. This proposal was opened to discussion until April 2004 and is being expected for final publishing. The abbreviation SIGFI, created in that document, refers to individual energy generation systems using intermittent sources. Examples of SIGFI are the Solar Home System and the Wind Home System. The minimum demand to be served by a SIGFI is 15 kWh/month. In a previous technical paper published by ANEEL [7] it is mentioned that a system with 300 Wp and 20 kWh/month production can give a good flexibility in terms of load considering the usual values of radiation in the country. The current must be mono-phase AC and the voltage must be the same as the one supplied by the grid in the municipality where the SIGFI will be installed (110V or 220V). Systems are grouped in classes where ensured energy to be supplied and autonomy are established. The table bellow shows the classes and values.

Table 1: Classification and supplying characteristics [7].

Other important technical features required are summarized bellow. • •

Energy consume indicator is needed only for SIGFI 30 or larger. DIC (Duracao da Interrupcao de fornecimento de energia por unidade Consumidora) data logger is needed. It has to record date and hour/minute of the interruption start and reestablishment start (the whole time the consumer staid without energy supply).

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

•

DIC data logger has to record also which component of the system (panel, battery, battery charge unit, inverter) has failed in each interruption. • DIC/month max allowed: 216 h (equivalent to 9 continuous days) • DIC/year max allowed: 648 h (equivalent to 27 continuous days) The supplied SIGFI has to include complete internal installation (cabling, ducts, circuit breakers, 1 socket, 3 light points with bulbs (recommended 20W fluorescent lamp), supports and other installation materials The utilities will give electricity tariff payment discount to families with consume lower than 80 kWh/month. Installation is going to be free of charge to any consumer [3], [6].

3 Evaluation Strategy To make an evaluation of the energy supplying options, the design of each option has to be done and costed. To make the design, the load has to be defined not only in terms of installed power but also in its use over the time (like per day, season or months for instance). Parallel to the load definition, a searching for the energy sources that are going to serve this load has to be done. Latter, the design and costs have to be compared economically, not only by their installation cost (same as capital cost) but also by their lifecycle cost. Lifecycle costs include the capital cost plus all the expenses during the whole life of the project like fuel costs for diesel generators and periodic replacement of batteries in SHS for instance. The logic of the evaluation strategy as outlined above is shown in figure 3.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Energy Resources Definition

Load Profile Definition

Basic Design

Costing

Processing: Operation Simulation and NPC Calculation

Evaluation of Performance and NPC

Y Improvements needed? N Sensitivity Analysis

Y Improvements needed? N Choice of the best option

Figure 3: Logic of the evaluation strategy.

As can be seen, a heavy work of calculation is needed both for the technical and economical aspects of the evaluation process. Therefore, the help of computer software is welcome. In this proposal of evaluation strategy, the search for software options with a good combination of results reliability, use access and cost was done. There are several softwares in the market: freeware or costly, more dedicated or more general use, more technical or more economical and so on. Our idea was to create an evaluation option that could be wide used with almost no cost and at the same time giving reliable results. With this objective, only one of the analysed software is not freeware but at least wide used: Microsoft Excel. Two dedicated spreadsheets2 were created to produce 2

For the sake of writing simplicity, "spread sheet" will be always referred also as "software" in the following sections.

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input data for freeware software dedicated to optimisation of stand-alone systems. This software is Homer with 4933 users in 161 countries around the world according to the official web site, developed and kept updated by the NREL National Renewable Energies Laboratory in USA. The other freeware software chosen was Radiasol from the Mechanical Engineering Department of the Federal University of Rio Grande do Sul, Brazil. It has a database of monthly averaged global horizontal solar radiation for several sites in Brazil or worldwide and makes, among other features, the correction to tilted surface radiation. Both softwares will be presented with more details in the case study sections ahead. With them, the strategy evaluation steps can be almost all performed with the help of a computer reducing the workload and calculation error probability. In Figure 3 the steps that are performed with software help are marked. The softwares used are summarized as follows: Homer: for system optimisation, main economic calculations and sensitivity analysis. Radiasol: for solar radiation data and tilt angle study. Sizing 5: software in Excel for load profile creation and SHS design. DICcalc: software in Excel to check if the energy shortage in hours per month and per year is bellowing the limits fixed by the Luz para Todos program.

4 The softwares used 4.1 Homer 4.1.1 Features Homer is system simulation and optimisation software. The user can model the system using any combination of these components: - Wind turbine - PV panel - Hydro turbine - Generator (wide range of fuels and option to include new ones) - Grid - Battery - Converter (BCC and/or inverter) - Electrolyser Loads can be: - Electrical (deferrable or not) - Thermal (it is possible to use only the heat from the gen-set)

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

The main screen is shown in figure 4.

Inputs

Results

Warning

Figure 4: Homer first screen with indication of main fields.

4.1.1.1 Simulation3 HOMER simulates the operation of a system by making energy balance calculations for each of the 8,760 hours in a year. For each hour, HOMER compares the electric and thermal demand in the hour to the energy that the system can supply in that hour, and calculates the flows of energy to and from each component of the system. For systems that include batteries or fuel-powered generators, HOMER also decides for each hour how to operate the generators and whether to charge or discharge the batteries. Therefore, time series data have to be given as inputs. They are needed for:

- Renewable energy resources (wind, solar, hydro). - Load Time series data can be imported from a text file (8,760 values are needed) or synthesized. The last option requires monthly averages, statistical parameters and random parameters that can be adjusted by the user. HOMER performs the energy balance calculations for each system configuration that was given to consider. It then determines whether a configuration is feasible, i.e., whether it can meet the electric demand under the conditions that were specified, and estimates the cost of installing and operating the system over the lifetime of the project. 3

Text extracted from the software help text with modifications by this author.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

The system cost calculations account for costs such as capital, replacement, operation and maintenance, fuel, and interest4. 4.1.1.2 Optimisation5 After simulating all of the possible system configurations, HOMER displays a list of configurations, sorted by net present cost (sometimes called lifecycle cost) that can be used to compare system design options. For each one, HOMER gives graphical data about all performance parameters and also the possibility to export data in timeseries format. If Homer finds that the best option requires a system component with size out of the specified sizes to consider, a warning appears in the screen asking to consider different sizes. This makes the use of Homer an interactive process. 4.1.1.3 Sensitivity Analysis6 When sensitivity variables are defined as inputs, HOMER repeats the optimization process for each sensitivity variable that was specified. For example, if wind speed was defined as a sensitivity variable, HOMER will simulate system configurations for the range of wind speeds that were specified. Sensitivity variables are known by this icon .. As can be seen in figure 4, there are 4 sets of inputs to Homer: - Load and Equipment - Resources - Economic - Constraints In the following sections, the inputs that are of interest in this thesis work are explained. 4.1.1.4 Load inputs The main screen of this input is shown in figure 5 .

4

Homer uses costs without inflation over the project lifetime. Therefore, the interest rate to be used is the real interest rate as will be seen in section 5.2.2.2. 5 Text extracted from the software help text with modifications by this author. 6 Text extracted from the software help text with modifications by this author.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 5: Homer load inputs screen.

The hourly load data can be entered manually or imported from a text file with 8,760 values. With the scaled data entry the user can multiply every hour of the 8,760 values by the same factor. In the present work we up scaled all values in 6% to include the wiring resistive losses into the load. In the example shown in figure 5 the annual average of 1.355 kWh/d was multiplied by 1.06 and gave the scaled annual average of 1.436 kWh/d. In order to make synthetic data more realistic, noise can be added both daily and hourly. For each hour of the year, Homer multiplies the load value by a daily randomly drawn factor and then by an hourly randomly drawn value. Figures 6, 7 and 8 show the effect of the noise on the load profile.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 6: Load as it is entered in the Baseline.

Figure 7: Load after daily noise is added.

Figure 8: Load after daily and hourly noise are added.

In the present work, daily and hourly noise values were changed until the annual peak reached the total load installed power. Everson Possamai, Sep/2004

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

4.1.2 PV panels inputs The main screen of these inputs is shown in figure 9 .

Figure 9: Homer PV inputs screen.

Sizes must be entered in rated Wp (MPP power at STC). The Derating Factor accounts for the losses due to different operating voltages, higher ambient temperatures and soiling of the panels in order to reduce the rated power. Slope is the latitude of the site as default and can be changed to any other value. Azimuth is the orientation of the panel. Ground reflectance has to be given in order to account for this other component of radiation in the tilted surface (the other two are direct and diffuse radiation, calculated by Homer). Capital costs are the installation costs. Replacement costs are those to be spent at the end of the modules lifetime and O&M (Operation and Maintenance) costs are those to be spent every year independent of the lifetime.

4.1.3 Wind turbine inputs Figure 10 shows the main screen of these inputs.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 10: Homer wind turbine inputs screen.

The power curve has to be given editing a new turbine type with the "New" button. The Power curve scaling factor can be used to adjust the power curve to real operational conditions when the average annual air density is too different from that used in the turbine test conditions. The wind speed scaling factor is not used because the wind speed to be entered in the wind resource is already corrected to the hub height. Capital costs are the installation costs, including everything necessary to erect the wind turbine. Replacement costs are those to be spent at the end of the turbine lifetime and O&M (Operation and Maintenance) costs are those to be spent every year independent of the lifetime.

4.1.4 Diesel gen-set inputs Figure 11 shows the main screen of these inputs.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 11: Homer generator inputs screen.

Lifetime and Minimum load ratio have to be obtained with the gen-set manufacturer. The Intercept value is the no-load fuel consumption of the generator divided by its rated capacity while the Slope is the amount of additional fuel used per hour as the load on the generator grows. Grid installation costs have to be included in the capital cost when a mini-grid is being analysed. If, at the same time, the user wants to know how the lifetime cost (NPC) of this mini-grid changes with the change in the grid length or change in the number of users connected than a sensitivity analysis in the capital and O&M costs can be performed. The sensitivity values must contain the increase in the grid installation costs.

4.1.5 Battery inputs Figure 12 shows the main screen of these inputs.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 12: Homer battery inputs screen.

Like the other components, there is also a screen to enter the capital, replacement and O&M costs as well as sizes to consider. The battery inputs are the most important data for Homer. All the calculations will, at the end, turn around the batteries. The internal algorithm takes in account these inputs to decide if the energy from the generation source has to go to the load or to charge the battery, if there is enough number of batteries to feed the load when the source is out and many others. Therefore, these inputs are very detailed and require a considerable amount of information. First, the capacity and lifetime curves have to be fed in. The first gives the amount of energy that can be drawn from a fully charged battery for every constant current until the end voltage reaches the minimum allowed by the manufacturer. The second gives the lifetime throughput of the battery (to be used for battery lifetime calculation). For each DOD and correspondent number of cycles to failure Homer calculates the lifetime amount of energy that the battery can deliver. Than it takes the average of all values ranging from 100% SOC to the minimum SOC allowed by the user and gives it as a suggested lifetime throughput. The user has to be careful informing the minimum SOC for two reasons: 1) Too low values will give lifetime throughputs higher than the battery can withstand in reality without having its electrodes permanently destroyed Everson Possamai, Sep/2004

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2) Too high values will make Homer suggest more batteries than really needed. In order to help the designer choose the right battery for the end user needs, we list bellow the features of the most used kinds of batteries for stand-alone applications: Car Battery, also called VLI battery - Should not be used in SHS [10]. - Designed to produce high current in short time and be charged also in short time (a very low probability case in SHS). - If there is no other choice, try to use truck or heavy duty equipment battery [10]. Sealed, VRLA, Maintenance Free, shallow-cycle - Have Calcium in the electrodes alloy - Recommended max DOD for sealed car battery: 25% [15] - Recommended max DOD for modified sealed car battery (or stationary): 60% [11] - Called maintenance free (sealed because of low water dissociation of the electrolyte) - Keep in mind that plastic casing is permeable to hydrogen and that no battery has completely sealed mounting junctions - It is also called VRLA battery: Valve Regulated Lead Acid battery because it has an internal pressure relief valve - Have low electrolyte amounts, often trapped in gels - High charge efficiency - SOC can usually be determined by measuring voltage - Disadvantages: more sensitive to higher temperature environment than conventional flooded batteries; relatively low energy density - Advantages: cheap initial cost (careful with the lifecycle cost!), light, easy to find. SPV maintenance free battery - SPV batteries are not sealed but do not require maintenance because they have internal membranes to avoid the H2 and O2 escape and at the same time promoting their internal recycling. - Have almost the same performance as the VRLA batteries (the only difference is that they are designed for high temperatures) - Have Calcium in the electrodes alloy - Recommended max DOD: the same as for VRLA. Flooded, also called deep-cycle or Pb-Sb battery - The amount of Antimony in the electrodes alloy is tried to be reduced to a minimum because this element increases water dissociation at high voltages - Have more Antimony than VRLA batteries - Recommended max DOD: 80% - High water dissociation - Have caps in order to allow access to the electrolyte and allow water refilling - May have catalyst caps to make the recombination of hydrogen and oxygen, being almost maintenance free in this case. Without this device, require periodic checking if the electrolyte level is above the electrodes and if the electrolyte density is correct. - Disadvantages: requires maintenance (distilled water refilling); offer hazards if mishandled (can spill acid or produce explosive mixtures of hydrogen and oxygen); bigger than VRLA battery. - Advantages: last longer and have more energetic storage capacity Everson Possamai, Sep/2004

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Opz Battery - Can be lead/calcium or lead/antimony alloys - Have tubular electrode construction, better than plate construction for deep discharge and high cycling stresses (they cause electrode mechanical stress or even material losses) - Recommended max DOD: 80% - Have the same features as listed in the flooded battery above plus higher lifetime. The other Homer inputs are: - Round trip efficiency: the round trip DC-to-storage-to-DC efficiency of the battery bank. - Float life: the maximum lifetime of the battery, regardless of usage. - Maximum charge rate: the battery's maximum allowable charge rate, measured in amps per amp-hour of unfilled capacity. - Maximum charge current: the absolute maximum charge current, in Amps.

4.1.6 Converter inputs Figure 13 shows the main screen of these inputs.

Figure 13: Homer converter inputs screen.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Costs regarding the BCC and consume meter have to be included in the converter costs inputs. When specifying the size, the designer has to check if the inverter power is higher than the peak load and that it can withstand the surge load (electric motors startup). Also, be careful with the components lifetime. If the project lifetime is, for instance, 20 years and the lifetime of the converter is also set to 20 years than Homer will assume an unnecessary change at the end of the project lifetime. In this example, the converter lifetime should be set to 21 years to avoid the problem. This is valid for all components lifetime inputs.

4.1.7 Solar and Wind resource inputs For solar resource, the inputs are the latitude of the site and daily average solar radiation for each month of the whole year (see figure 14). Time-series data can also be imported from a text file with 8,760 values.

Figure 14: Homer solar resource inputs screen.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

For wind resource, the inputs are the monthly average values of wind speed at the hub height, Weibull k factor, hour of peak wind speed and two other statistical parameters7 (see figure 15): - Autocorrelation factor: a measure of the randomness of the wind. Higher values indicate that the wind speed in one hour tends to depend strongly on the wind speed in the previous hour. Lower values indicate that the wind speed tends to fluctuate in a more random fashion from hour to hour. This parameter is influenced by local topography. Autocorrelation factors tend to be lower (0.70 - 0.80) in areas of complex topography and higher (0.90 - 0.97) in areas of more uniform topography. - Diurnal pattern strength: a measure of how strongly the wind speed depends on the time of day. In most locations, for example, the afternoon tends to be windier than the morning. Higher values indicate that there is a relatively strong dependence on the time of day. Lower values indicate that the wind speed is not strongly related to the time of day.

Figure 15: Homer wind resource inputs screen.

As for the solar resource, wind data can also be imported from a text file.

7

Text extracted from the software help text with modifications by this author.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

4.1.8 Economic and Constraint inputs Only three inputs are of interest in this thesis work: - Real interest rate - Lifetime of the project - Maximum allowed capacity shortage

4.1.9 Results The result of Homer simulations is system designs sorted in decreasing order of NPC. A typical result screen looks like the one in figure 16.

Figure 16: Homer typical presentation of results.

For each option, the main useful information that Homer gives is the number of components considered, total capital, NPC and Cost of Energy. This last one is the NPC transformed into annuities during the project lifetime divided by the total served load during this time. It's a demonstration of how much each kWh will cost considering the whole lifecycle cost.

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4.2 Radiasol This software uses the global horizontal radiation (in monthly averages), latitude and date as bases for all calculations. With this information, it calculates the extraterrestrial, direct and diffuse radiation and then give the global radiation for every tilt angle and orientation desired by the user for every day and hour of the year or monthly averages. It has also a database where the user can choose data from 2072 sites around the world. Among these, the user can find 354 sites from Brazil (the data come from OLADE Organizacion Latinoamericana de Energia). Main screen is shown in figure 17.

Figure 17: Radiasol main screen.

4.3 Sizing 5 As mentioned before, Homer is a software for system optimisation using timeseries data. Therefore we developed a tool in Excel to define the load profile in hourly values as well as a tool to make the basic design of photovoltaic systems. For wind systems, it was not necessary to develop such a tool since the turbines available in the market have fixed sizes (not like PV where one can simple assemble more or less modular panels in series or parallel). Battery bank and inverter are the same for WHS as for SHS systems since both are designed towards the load.

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The load profile is obtained selecting the individual loads power in the load menu and copying them to the load inputs. There, the hourly usage of each individual load has to be filled in six typical days of the whole year: - Week and weekend in summer - Week and weekend in middle season - Week and weekend in winter. It is advisable to fill this table together with the end user, trying to get his/her behaviour on energy usage. Figure 18 shows one example of how the table has to be filled8.

SUMMER WEEK

load pattern device power/unit [W] total of units total installed power (W) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

fluorescent tube lamp 20 3 60 units ON Wh 0 0 0 0 0 0 1 20 1 20 0 0 0 0 0 0 0 0 0 0 0 0 2 40 3 60 1 20 0

Figure 18: Sizing 5 loads input screen example (fill in the green cells).

The hourly data will be used for the basic PV sizing and load inputs to Homer. Once the load inputs table is filled, the load profile and load summary are shown like in the figures 19, 20 and 21, allowing the visualization of the profile and its components as well as the total daily and monthly energy demand.

8

In all the spreadsheets we created in Excel, green colour means cells to be filled by the user (input data).

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Daily load in a typical summer week day 180 160

power (W)

140 120

fluorescent tube lamp 0

100

incandescent lamp (DC) 0 radio 0

80

TV color 14" w/ receiver 0

60

refrigerator

40 20

22

20

18

16

14

12

10

8

6

4

2

0

0

time

Figure 19: Sizing 5 presentation of the load profile in a typical day.

SUMMER

incandescent lamp (DC) radio 0% 2%

fluorescent tube lamp 12%

TV color 14" w / receiver 13%

refrigerator 73%

Figure 20: Sizing 5 information of the share of each load in the daily load profile.

total AC load power energy (Wh) SUMMER MIDLE SEASON WINTER

SUMMER WEEK DAY SUMMER WEEKEND DAY MIDLE SEASON WEEK DAY MIDLE SEASON WEEKEND DAY WINTER WEEK DAY WINTER WEEKEND DAY

refrigerator 28,905 25,991 24,205 demand Wh/day 1,305.5 1,381.5 1,328.4 1,404.4 1,368.8 1,444.8

240 TV color 14" w/ receiver 5,168 5,168 5,168 peak load W 210.0 210.0 230.0 230.0 230.0 230.0

W radio 900 900 900

incandescent fluorescent lamp tube lamp 0 4,800 0 8,400 0 11,400 demand Wh/day SUMMER 1327.2

TOTALS 39,773 40,459 41,673 demand kWh/m 39.8

MIDLE S

1350.1

40.5

WINTER

1390.5

41.7

ANNUAL AVG

1354.5

40.6

Figure 21: Sizing 5 load summary presentation.

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The initial design of PV systems is based on the method proposed by SANDIA National Laboratories of USA [10]. This is the same method described in the Engineering Manual for PV Systems9 edited by CEPEL - CRESESB in Brazil. Three main issues about this method are listed bellow: - The load is expressed in amperes instead of watts. This is done because it is easier to make a meaningful comparison of PV module performance, i.e., ask for PV modules that will produce 30 amperes at 12 volts and a specified operating temperature rather than try to compare 50 watt modules that may have different operating points. - Best tilt angle study is performed. For each month of the year, the average daily load (in Ah/day) is divided by the total daily radiation over surfaces with tilt angle equal to the latitude, latitude - 15° and latitude + 15° (in kWh/m2/day, that is equal to full sun hours per day10), giving the current requirement from the PV array. For each tilt angle, the highest current is taken and then the smallest of the three tilt angles is chosen. The first operation gives the worst month for each tilt angle and the second operation gives the best tilt angle. The values of global horizontal radiation are obtained with Radiasol. - Corrections in performance due to cell operating temperature, battery operating temperature, efficiency of the components and wiring losses are taken into account. The main screen of the basic design is shown in figure 22.

9

Translation to English of the original title in Portuguese. Full sun hours means hours with solar radiation equal to 1 kWh/m2.

10

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems η

TOTAL

70% AC load

generator derate BCC

installed power W SUMMER 240 MIDLE S 240 WINTER 240

inverter

100%

90%

80% battery (Wh effi.) wiring Impp (STC) Vmpp (STC) Voc (STC) Isc (STC) module operating T load voltage minimun allowed autonomy max battery DOD capacity of the selected battery (at 25*C, 20h) battery lowest ambient temperature nominal DC system voltage nominal battery voltage peak current

4.38 16.8 21 4.86 40 110.0 2 60% 105.0 15 24 12 10.0

A V V A °C V days

design current

522

L + 15 47 5.15 4.88 4.78 4.41 3.88 2.96 3.41 3.98 4.16 4.98 5.22 5.39

corrected Ah/day 79.2 80.5 83.0

design current A 15.38 16.23 16.56 18.26 20.75 27.17 24.31 20.85 19.95 16.19 15.43 14.93

max 0.00 0.00 0.00 0.00 0.00 27.17 0.00 0.00 0.00 0.00 0.00 0.00

97%

-72.5 mV/°C -72.5 mV/°C 0.8 mA/°C

Ah °C V V A

corrected Ah/day J F M A M J J A S O N D

surge

Ah load Ah/day 61.4 62.5 64.4

design current A 12.60 13.92 15.08 18.09 22.16 29.02 26.19 21.24 18.60 14.23 12.80 11.99

L - 15 17 6.28 5.69 5.25 4.45 3.63 2.78 3.17 3.91 4.46 5.66 6.29 6.72

79.18 79.18 79.18 80.55 80.55 80.55 82.96 82.96 82.96 80.55 80.55 80.55

max 0.00 0.00 0.00 0.00 0.00 29.02 0.00 0.00 0.00 0.00 0.00 0.00

L 32 6.22 5.46 4.78 3.79 2.94 2.28 2.56 3.26 4.00 5.30 6.16 6.71

design current A 12.73 14.50 16.56 21.25 27.40 35.27 32.36 25.42 20.75 15.19 13.08 12.00

max 0.00 0.00 0.00 0.00 0.00 35.27 0.00 0.00 0.00 0.00 0.00 0.00

Impp Vmpp Pmpp Isc Voc

STC 4.4 16.8 73.6 4.9 21.0

operation 4.4 15.7 69.0 4.9 19.9

27.2 A

Batteries minimun allowed autonomy battery derating factor required battery capacity batteries in parallel batteries in series total batteries system battery capacity usable battery capacity

2.0 1.0 291.1 3.0 2.0 6.0 315.0 189.0

days Ah

Ah Ah

PV Array derated array design current operational Impp modules in parallel voltage required to charge batteries operational Vmpp modules in series total modules

27.2 4.4 6 28.8 15.7 2 12 operational array current 26.4 array short circuit current 29.2 array voltage 31.4 array open circuit voltage 39.8

A A V V

rated 26.3 29.16 33.6 42

A V Wp A V

A A V V

BCC array short circuit current corrected array current

operational 29.2 36.5 A

rated 29.2 A

Figure 22: Sizing 5 design screen.

The basic data for all the main components are given in order to make their specifications for quotation.

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4.4 DICcalc The Light for All program established indirect maximum values for monthly and annual LOLP (Loss of Load Probability). We say indirect because the limit is not a fraction (hours of loss of load / 8,760) but just a number of hours. This number is called DIC (Energy Supply Interruption per Consumer11) that, when divided by the total number of hours in a year or a given month, gives the LOLP parameter per year or month. One drawback of Homer is that it does not use the LOLP parameter to quantify energy supply interruption but rather what it calls "maximum annual capacity shortage". This is the maximum allowable value for the ratio between the unmet load by the total annual demanded electric load, both in kWh. To deal with the conversion from one to the other concepts we created the DICcalc Excel spread sheet. After HOMER process the input data and gives the least NPC option, we export the time-series of unmet load to DICcalc which than will count the number of hours among the 8,760 values and return the DIC value per month and per year, as well as giving the LOLP value. Another important remark is that the DIC number includes hours with supply interruption due to maintenance. HOMER does not take into account this kind of shortage. We created the option in DICcalc to make this discount and thus have the effect corrected in the evaluation. Figure 23 shows the results screen in DICcalc.

OK OK OK OK OK OK OK OK OK OK OK OK more than allowed

DIC

350 300 hours / month

DIC due to performance J 35 F 0 M 52 A 43 M 0 J 30 J 45 A 41 S 0 O 25 N 0 D 32 year 303

250

DIC due to performance

200

limit

150 100 50 0 J

F

M

A

M

J

J

A

S

O

N

D

year

Standards DIC: days for maintenance DIC due to performance LOLP due to performance LOLP total

per month 9 days 5 days 96 hours 13% 30%

per year 27 days 15 days 288 hours 3% 7%

Figure 23: DICcalc results screen

11

Translation to english of Duracao da Interrupcao de fornecimento de energia por unidade Consumidora, in Portuguese.

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5 Economic analysis of costs: the theory behind Homer 5.1 Interest Rates and Inflation When the costs of two projects have to be compared, it is correct to consider not only the installation costs but also the life cycle costs, in other words, not only the initial costs but also the costs that will happen during the whole life of the project. In this case, there are two main factors influencing the calculation that have to be considered carefully: - Nominal Interest Rate (inom ). - Inflation (j)

5.1.1 Nominal Interest Rate Nominal Interest Rate is the interest rate that the investor would gain if had applied its money in the next best investment alternative available. For small household investments, it can be the interest rate paid by a bank investment with a very low risk. For a utility that is obliged by law to install rural electrification in remote areas, it can be the interest rate that it would get if it had applied its money in the bank in a low risk investment. Low risk bank investment is chosen because the utility is comparing this option against low investment return of its money in the project due to technical or operational difficulties typical of remote areas electrification. In other words, it cannot expect high gains with the project because of the technical aspects and also because the government wants low electricity tariffs for the poor people in the far countryside. The list in Appendice 1 shows the interest rates paid by the investment opportunities of Banco Itau in Brazil. In this list, the investment risk levels are indicated. The interest rates according to the investment risk level can be summarized as: No Risk investment: Low Risk investment: High Risk investment:

9,16% / year 13% / year 48% / year

The use of a nominal interest rate of 13% / year is therefore appropriate for the sake of this thesis work.

5.1.2 Inflation Inflation is the up scaling of prices, lets say every year, due to factors like the economic situation of the country, the influence of the currency exchange rate on prices and the increasing demand for a fixed production, for instance. In Brazil, the table 2 shows the official inflation of the last 4 years (the used index was the IGPM of the Fundacao Getulio Vargas).

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year 2000 2001 2002 2003

inflation 9,95 % / year 10,38 % / year 25,31 % / year 8,71 % / year

Table 2: Official inflation index of the last 4 years in Brazil (source: Banco Central do Brasil).

The year 2002 was year of federal government elections12 with a strong political effect on prices. Investors, enterprisers and the society behaviour were strongly influenced and so the inflation index. It is reasonable to make an average of the inflation indexes not considering 2002, assuming that it was not a representative year for inflation in Brazil. In this case, the average of the other 3 years is 9,68% / year. The inflation of the last 12 months was 9,61% / year in middle July 2004 (reference: Banco Itau). Therefore, there are enough information to Justify the use of 10% / year as the inflation index in this thesis work. When there is inflation, the nominal interest rate has the upscaling of prices effect already considered. To have the real gain of an investment, the inflation has to be discounted and the real interest rate (r) is used. The equations governing this operation are: qinom = qir * qj where qinom = 1 + inom qir = 1 + ir qj = 1 + j ir =

inom − j ≅ inom − j 1+ j

5.2 Operations with Values As mentioned in the topic before, the analysis of an investment takes in consideration the costs incurred in the beginning and also during the lifetime of the project. To compare the investment that is being studied with the other opportunities it is necessary to have a common basis or, in other words, a common reference in time. There are two ways to establish a reference: - Convert all values to the time 0 (Present Value PV) - Convert all values to constant annual values. In both cases, operations with the values are needed. They are expressed bellow in graphical and mathematical form together with examples. 12

There are federal governmental elections every 4 years in Brazil

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5.2.1 Future Value (FV) of a Present Value (PV) after n times

PV

FV 0

1

2

3

4

5

6

7

8

FV = PV × (1 + i ) n

9

...

Figure 24: Conversion from PV to FV.

5.2.1.1 Example a What is the value, in the year 4, of a Solar Home System battery that has a current value of € 50,00, considering that the inflation of this component price is 10% / year? PV = 50 i = j = 0,1 n=4 FV = 73,21 5.2.1.2 Example b What is the value, in the year 4, of € 1.000,00 invested today in a bank application giving a nominal interest rate of 16% / year? PV = 1.000 i = inom = 0,16 n=4 FV = 1.810,64

5.2.2 Present Value (PV) of a known value in the time n

PV

FV

0

1

2

3

4

5

PV =

6

7

8

9

...

FV (1 + i ) n

Figure 25: Conversion from FV to PV.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

5.2.2.1 Example a Consider that we are analysing the life-cycle cost of a Solar Home System (SHS) project that has one battery. This battery has to be replaced in the end of year 4. The current cost of this battery is € 50,00 and the inflation for the battery cost is 10% / year. The nominal interest rate we are using in the analysis is 16% (=~10% inflation + 6% real interest rate) because the owner of the SHS would earn 16% / year (=~10% inflation + 6% real earnings) if he had applied his money in other project. What is the PV of this component replacement in our life cycle cost analysis? 0,10

50

73,21

0

1

2

3

4

5

6

7

8

9

...

Figure 26: Conversion of PV to FV in the example.

PV = 50 i = j = 0,1 n=4 FV = 73,21 (it is the price of the battery in year 4 due to inflation)

0,16

40,43

73,21

0

1

2

3

4

5

6

7

8

9

...

Figure 27: Conversion of FV to PV in the example.

FV = 73,21 i = inom = 0,16 n=4 PV = 40,43 5.2.2.2 Example b In example 2.2a we inflated the value of the battery from today to year 4 and then found the value of this battery back to today (PV) considering the opportunity interest rate. We would have found the same result if we had moved the value to time 4

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

without inflation and then went back to today using the real interest rate (the rate without inflation inside):

50

50

0

1

2

3

4

5

6

7

8

9

...

4

5

6

7

8

9

...

0,0546

40,43

50

0

1

2

3

Figure 28: Placing one value in the timeline without inflation and calculating its PV.

FV = 50 i = ir = 0,0546 n=4 PV = 40,43 Note that this operation did not mean that there was no inflation. Inflation was just considered in a different way.

5.2.3 Present Value (PV) of an increasing series and the opposite

PV

A

0

1

A*qi

2

A*qi^2

3

A*qi^3

i here can be: j if just inflation is responsible for the increase or other rate if inflation plus something else is up scaling the values

4

(1 + j ) n 1− (1 + inom ) n PV = A * inom − j

A = PV *

inom − j (1 + j ) n 1− (1 + inom ) n

Figure 29: Operations with series of values.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

5.2.3.1 Example Consider that a Maintenance cost of € 500,00 / year is subjected to an inflation of 10% / year during 4 years. Which is the PV of this cost? The nominal interest rate is 16% / year. A = 500,00 j = 0,10 inom = 0,16 n=4 PV = 1.594,92

5.2.4 Present Value of an annuity (constant value) and the opposite

PV

A

0

1

A

2

A

3

A

4

A

5

...

Figure 30: Annuity.

In this case, the inflation of the annual value is zero. The formulas for these operations are the same as for the previous case but the inflation (j) is set to zero. 5.2.4.1 Example A bank loan is contracted for a project and the annual payment is € 500,00 for 4 years. The nominal interest rate being used in this project financial analysis is 16% / year. Which is the PV of these payments? A = 500,00 j=0 inom = 0,16 n=4 PV = 1.399,09

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

5.3 Summary of rates Summary of the rates for the sake of thesis work calculations: Inflation (j): 10 % / year Nominal Interest Rate (inom): 13 % / year Real Interest Rate (ir ): 3 % / year

6 Case study 6.1 Site selection As mentioned before, the strategy summarized in figure 3 with the help of the softwares chose is exemplified through a case study. We chosen a location in Brazil that would fit into the premises for electrification established by the Light for All program. These premises can be summarized as follows. - Priority will be given to the use of local labour force and national equipment, which, where possible, are manufactured close to the attended communities. - Priority will be given to projects in areas with HDI bellow the national average Since we come from Rio Grande do Sul, south Brazil, having most of our contacts in this area and considering the priority given to local labour force and equipment, the case study site is located in this area. Besides this, rural electrification in Rio Grande do Sul still has a big room for improvement. Figure 31 shows the number of households without electricity access per state of the federation. Rio Grande do Sul is in the 7th place in number of households without electricity in rural areas being in front of wild natural conditions states like the Amazon, Acre and Rondonia. Reasons for this are its higher population density and the areas with low HDI in the southwest part of the state. The total number of households in the rural area without electricity is around 75.000.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Rural households without access to electricity

Figure 31: Number of households without electricity access per state of the federation, 2003 [5].

Figures 32 and 33 bellow show the political subdivisions of Rio Grande do Sul with their HDI and electricity access index. Red areas are those with values bellow the national average. National Averages: IDH: 0.766 Electricity access index: 93,48%

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

HDI

Figure 32: Households without electricity access

Figure 33: HDI and electricity access index in Rio Grande do Sul [4].

In these maps, we looked for an area that would be in red colour and at the same time would have good solar and wind resources, to explore more the capabilities of the proposed electrification evaluation strategy. It was necessary to check the wind and solar atlas whose maps are shown bellow.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Figure 34: Annual average wind speed at 50m height (m/s) [8].

Figure 35: Annual average solar radiation on horizontal surface [9]

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

The best combination was found in coastal area of the south half of the state, in the municipality of Sao Jose do Norte. Its position in the map and relevant social, economical and political data for this thesis work are summarized bellow. The data were obtained in reference [4].

Latitude: 32,01° S Longitude: 52° W Figure 36: Position of Sao Jose do Norte in the map of Rio Grande do Sul.

Municipality

% of people living in households connected to the public potable water pipeline

% of people living in households with electric energy

São José do Norte (RS)

70

80

Table 3: Access of potable water and electricity in Sao Jose do Norte.

HDI

0.703

Position of Sao Jose do Norte in terms of HDI among other municipalities: National level: intermediate (2,934 municipalities are better and 2,572 are worst) State level: bad (461 municipalities are better and 5 are worst)

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems demographic density: urban area. rural area: 20% of the population without electricity: inhabitants per household: households without electricity in the rural area:

23,796 inhab. 17,294 inhab. 6,502 inhab.

1,135 km² 86 km² 1,049 km²

21.0 inhab/km² 200.0 inhab/km² 6.2 inhab/km²

4,759 inhab. 4.0 inhab.

767 km²

6.2 inhab/km²

1,189.8 un

Table 4: Population and electricity access data of Sao Jose do Norte

Obs.: - 200 inhabitants/km2 in the urban area is an assumption based on typical values of cities with similar size to Sao Jose do Norte. - We considered that all the population without electricity live in the rural area.

6.2 Solar resource input The following data were extracted from the Radiasol software. Radiation data: Data from Rio Grande (very close to Sao Jose do Norte) Latitude: 32.02° S Longitude: 52,08° W JAN

FEB

MAR

APR

MAY

JUN

6.222 JUL

5.46 AUG

4.782 SEP

3.79 OCT

2.94 NOV

2.284 DEC

2.564

3.264

3.998

5.304

6.158

6.71

Table 5: Global horizontal radiation (kWh/m² day) assumed for Sao Jose do Norte.

6.3 Wind resource input Data from the area were extracted from the Wind Atlas of Rio Grande do Sul [8] and OCACIA [14]. Average monthly wind speeds from measurements at 10 m height [14] converted to 8.5 m height13 using:  Z  ln  Z U Z = U 10  0   10  ln   Z0 

13

where:

8.5 m is the hub height of the wind turbine that we received commercial quotation.

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Z is the desired height (m), Z0 is the rugosity of the site (m), Uz is the calculated wind speed for the desired height (m/s), U10 is the given wind speed at the given height (m/s).

J F M A M J J A S O N D

0.08 m Z10 10 m 5.44

Z 8.5 m 5.26

5.22

5.04

4.65

4.49

4.88

4.72

5.28

5.10

4.81

4.65

5.14

4.97

4.81

4.65

5.48

5.30

5.74

5.55

5.74

5.55

4.94

4.77

7 6 wind speed (m/s)

Zo

5 4

10m height 8.5m height

3 2 1 0 J

F

M

A

M

J

J

A

S

O

N

D

Figure 37: Correction of the wind speed due to height.

Weibull k factor: 2.1 Orography: Flat with short dunes Rugosity Z0 = 0.001 to 0.08 m

Figure 38: Panoramic view of Sao Jose do Norte and rugosity map [8].

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Figure 39: Daily wind speed profile for every month in the vicinity of Sao Jose do Norte [8].

6.4 Load input for a single household Home Systems using renewable energy sources in developing countries normally are designed for low energy demands. This is usually reffered to high installation costs when compared to simple grid extension and also because of low acquisitive power of the system users, restricting their possibility to have more household appliances. SANTOS [11] found that the energy demand in poor rural areas of southeast and northeast Brazil stays in the range up to 14 kWh/month for households without refrigerator and from 30 to 70 kWh/month when the households have this appliance. OCACIA et. al [12] suggests that 50 kWh/month satisfy the light, communication and refrigeration needs in households in the rural areas of Rio Grande do Sul, being the refrigerator responsible for 65% of the total demand. In another paper about PV and PV + wind hybrid home systems [13] the same authors proposed and successfully tested different configurations to serve a load of 40 Kwh/month again in the countryside of Rio Grande do Sul. This load was composed by compact lamps of 15 W, 290 l refrigerator and colour TV 14". Based on these references, we created a load profile using the Sizing 5 software, whose inputs14 and results are shown bellow.

14

Nominal power of appliances obtained from: Refrigerator: Consul (Brazilian manufacturer) model CRA28B, www.consul.com.br TV: Philco (Brazilian manufacturer) model TP1454, www.philco.com.br Antennae receiver: Century (Brazilian manufacturer) model USR1900, www.centurybr.com.br Lamps and Radio: Haars, Klaus; Electricity from Sunlight; GTZ; 1997.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems units 1 1 1 3

appliances refrigerator 280 l TV color 14" w/ parabolica antena receiver radio fluorescent tube lamp

nominal power (W) total power (W) 94 94 36 + 40 76 10 10 20 60 Sum

SUMMER WEEK DAY SUMMER WEEKEND DAY MIDLE SEASON WEEK DAY MIDLE SEASON WEEKEND DAY WINTER WEEK DAY WINTER WEEKEND DAY

demand Wh/day 1,305.5 1,381.5 1,328.4 1,404.4 1,368.8 1,444.8

peak load W 210.0 210.0 230.0 230.0 230.0 230.0

240

SUMMER

demand Wh/day 1327.2

demand kWh/month 39.8

MIDLE S

1350.1

40.5

WINTER

1390.5

41.7

ANNUAL AVG

1354.5

40.6

Table 6: Case study load summary.

Figure 40: Case study load profile of every representative day of the year.

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SUMMER

radio 2%

compact lamps 12%

TV + receiver 13%

refrigerator 73%

MIDLE SEASON

compact lamps 21% radio 2% TV + receiver 13%

refrigerator 64%

WINTER

compact lamps 27%

radio 2%

refrigerator 59%

TV + receiver 12%

Figure 41: Case study load share of the appliances.

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6.5 Load input for a village To define this load, we assumed that all households in the village would have the same load profile. The single household load profile was then up scaled to the number of houses in the considered village using Homer scaled annual average feature and noise was added in order to make it more realistic. In real life, a detailed study on the village load profile should be done. Since the objective of this work is to define an evaluation strategy for electrification rather than a specific case study, we believe that the assumptions done are reasonable. Regarding the village size, we assumed a number of 30 houses as a representative value of possible real cases. This came from personal opinion of the professionals we were in contact during the work and also observations in the countryside of Rio Grande do Sul in recent years. Taking in account that the village has 30 households with same load profile each and that the single house peak is 240 W we found that the village peak load would be 0.24 x 30 = 7.2 kW. Adding 6% wiring losses, the operational peak load becomes 7.63 kW. Surge load (when refrigerators turn on) was not considered because the contribution of a single refrigerator in the total load is very small and the probability that several ones start at the same fraction of second is also very small. Base load for a single household is 94 W thus, for the village, it becomes 0.094 x 30 x 6% =~ 3 kW.

6.6 Economics and Constraints inputs As explained in section 5, the real interest rate for this work is assumed as 3%/year. Project lifetime is assumed as 20 years. Maximum annual capacity shortage is initially assumed as 2% and DIC checking with DICcalc is done for each system option. If DIC goes over the standard values or is too low than another maximum annual capacity shortage is given as input and another simulation is done. DIC over the standard is not allowed by the Light for All program and too low means that the system is over dimensioned. To get information about components availability in the market and updated costs in Brazil we counted with the support of three Brazilian companies who accepted the invitation to participate in the work sending their quotations and technical / commercial advise. These companies are15: - IEM Intercambio Electro Mecanico, in Rio Grande do Sul (SHS) - Stemac Grupos Geradores, in Rio Grande do Sul (Diesel gen-set) - Enersud Industria e Solucoes Energeticas, in Rio de Janeiro (WHS)

15

See also their contact data in the Appendice 9.2

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Prices and specifications were also obtained in the Internet and in the following web addresses: - Acumuladores Moura, www.acumuladoresmoura.com.br (stationary batteries) - Sol e Vento Energia Alternativa, www.energia-alternativa.com.br (BCCs, inverters and gasoline generators) Costs in Euro (€) or Dollar (US$) were converted into the Brazilian currency Real (R$) using the following exchange rates16: 1 € = 3.7 R$ 1 US$ = 3 R$

6.7 Components: characteristics and costs 6.7.1 PV generator The involved costs are summarized figure 42. The selected module was the Shell 80 Wp due to its lowest cost per Wp. Its rated (STC) characteristics are: PMPP = 80 Wp; IMPP = 4.57 A; VMPP = 17.5 V; ISC = 4.85 A; VOC = 21.8 V Temperature effect on performance is: ∆VOC = - 72.5 mV/°C; ∆VMPP = -72.5 mV/°C; ∆ISC = 0.8 mA/°C Source

PV generator costs Modules Manufacturer Shell Shell Shell Shell

model 50 75 80 (selected) 100

Imported? imported imported imported imported

Wp 50 75 80 100

R$ 1,400.00 2,080.00 2,120.00 2,975.00

R$ / Wp 28.0 27.7 26.5 29.8

€ / Wp 7.57 7.50 7.16 8.04

1,620.00 R$

Mounting hardware + installation + transport

150.00 R$/year

O&M Homer PV inputs

IEM IEM IEM IEM IEM IEM

Costs size kWp 0.08 0.64 1.2

capital replacement R$ R$ 3,740.00 3,740.00 18,580.00 18,580.00 33,420.00 33,420.00

O&M R$ / y 150.00 150.00 150.00

capital = modules cost + mounting + installation + transport Properties lifetime derating factor tracking system slope azimuth ground reflectance

20 94 no 47 180 20

y % ° ° %

estimate Sizing 5 Sizing 5 (facing north) (grass ground)

Homer library

Figure 42: Characteristics, costs and Homer inputs of the PV modules. 16

As from August 2004 using Yahoo Finance currency converter, www.yahoo.com/finance

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6.7.2 Battery We found basically two options of batteries in the Brazilian market that could be used in stand-alone systems: - Shallow-cycle sealed battery VRLA for heavy duty vehicle applications - Stationary shallow-cycle battery with internal gases recycling - Car battery We did not found any flooded deep-cycle battery with catalyst or other gases recycling device for solar home systems applications. It seems that this is a market opportunity for battery manufacturers or importers considering that the Light for All program will increase the demand for this kind of product. We also calculated how much one of these batteries should cost in order to have the same lifetime cost of the national shallow-cycle ones. The result was R$ 490.00 per battery (29% more expensive than the shallow-cycle one). Deep-cycle batteries with or bellow this price would be a better choice for use in stand-alone systems. The following considerations were done in the cost calculation: cost End-of-life value17 Lifetime Project lifetime

Shallow-cycle battery R$ 380.00 R$ 19.00 4 years 20 years

deep-cycle battery R$ 490.00 R$ 24.50 6 years 20 years

It is important to mention that this battery have to be easily available in local markets, otherwise even with better lifecycle cost it will not be used in stand alone systems just because of lack of availability. Regarding battery capacity, we choose one with a weight that could be manually handled without the need of carts.

17

5% of the capital cost when sold to recycling (source IEM).

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Battery costs R$ / un 380.00 20.00 380.00 19.00 0.00

capital cost installation replacement end of life value O&M

Moura

Homer battery inputs Costs qt un 1

capital R$ 400

replacement R$ 380

O&M R$ / y 0

capital = battery capital cost + installation replacement = capital - end of life value

life time curve DOD cycles 18 2500 20 2100 25 1500 30 1200 33 1000 40 750 50 500 60 400 70 350 80 300

SPV stationary battery Moura 12MC105 25 kg 10 y 105 Ah 12 V 80 % 40 % 10 h 0.2 A/Ah 21 A

[15] pg 23.80 Moura Moura [15] [11] pg 134 [15] pg 23.7 Moura + [15] pg 23.72 and 23.73 IEM + [15] pg 23.72 and 23.73

Lifetime curve

number of cycles to failure

type manufacturer model weight float life nominal cap nominal voltage round trip efficiency min SOC nominal time max charge rate max charge current

3000 2500 2000

Moura

1500 1000 500 0 0

20

40

60

80

100

DOD (%)

Capacity curve 250 200

Moura

150 Ah

capacity curve Ah A 105 5.3 94 9.4 93 10.3 91 11.4 89 12.7 86 14.3 83 16.6 82 18.2 80 20.0 78 22.3 75 25.0 72 28.8 69 34.5 64 42.7 58 58.0 53 70.7 47 94.0 36 144.0

100 50 0 0.0

50.0

100.0

150.0

200.0

250.0

300.0

A

Figure 43: Characteristics, costs and Homer inputs of the battery.

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6.7.3 Converter Source

Converter costs Inverter (modified sine wave) Power (W) IzzyPower HTE150 150 Xantrex Jazz 300 240 Xantrex Jazz 500 400 StadtPowert Prowatt800 1000

[11] Imported? imported imported imported imported

installation + mounting hardware + transport

R$ Details 370.00 600.00 750.00 1,380.00

Energia Alternativa Energia Alternativa Energia Alternativa Energia Alternativa

1,080.00

IEM

BCC

45

imported

R$ Details 2,100.00 only BCC Energia Alternativa 1 year data logging + BCC + 6,050.00 energy meter Steca (IEM)

Steca Power Tarom 2070 (for hybrid systems)

70

imported

BCC + 7 weeks data-logging 7,040.00 + consume meter Steca (IEM)

Consume meter

15

national

Trace C40 Steca PA Tarcon Seriel + Solarix Tarom

Current (A) Imported? 40 imported

180.00

IEM

Homer converter inputs size kW

capital replacem. R$ R$

O&M R$ / y

Inverter + 40A BCC, no data logging, with consume meter: 0.15 3,550.00 3,550.00 0.24 3,780.00 3,780.00 0.4 3,930.00 3,930.00 1 4,560.00 4,560.00

0 0 0 0

Inverter + 45A BCC, with data logging: 0.15 7,500.00 0.24 7,730.00 0.4 7,880.00 1 8,510.00

0 0 0 0

7,500.00 7,730.00 7,880.00 8,510.00

Inverter + 70A BCC for hybrid systems, with data logging: 0.15 8,490.00 8,490.00 0.24 8,720.00 8,720.00 0.4 8,870.00 8,870.00 1 9,500.00 9,500.00

0 0 0 0

Only the inverter 1

1,380.00

lifetime efficiency

1,380.00 10 years 90%

0 Steca IEM

Figure 44: Characteristics, costs and Homer inputs of the converter.

6.7.4 Wind turbine Two sizes of 100% Brazilian made wind turbines for home systems were considered for further optimisation in Homer. Their characteristics are: Everson Possamai, Sep/2004

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Gerar 164 Gerar 210 rotor diameter 1.64 m 2.1 m power at 12.5 m/s 400 W 700 W number of blades 3 3 kind of blade twisted, 5 profiles twisted, 5 profiles cut in wind speed 2 m/s 2 m/s control active stall active stall cut off wind speed 20 m/s 20 m/s cut off control pitch control pitch control output voltage 12 / 34 or 48 V 24 / 48 V

Figure 45 bellow shows the summary of costs and Homer inputs. Source

Homer wind turbine inputs Costs qt

capital replacem. R$ R$

O&M R$ / y

Gerar 164

1

3260

3110

102

Enersud

Gerar 210

1

3680

3530

102

Enersud

capital = wind turbine + BCC + tower + fundament + installation hardware + labour + transport replacement = capital - fundament Power curve Power curve kW 0.00 0.35 0.44 0.45 0.40 0.25 0.23 0.23

Gerar 210 v (m/s) 2.8 5.6 8.3 9.7 11.1 13.9 19.4 20.8 20.8 22.2

kW 0.00 0.15 0.30 0.40 0.50 0.89 0.99 0.35 0.35 0.35

lifetime

Gerar 164 Gerar 210

1.20 1.00

power (kW)

Gerar 164 v (m/s) 3.9 12.5 16.7 17.5 19.4 20.0 20.8 23.6

0.80 Enersud

0.60 0.40 0.20 0.00 0.0

5.0

10.0

15.0

20.0

25.0

wind speed (m/s)

10 years

Figure 45: Characteristics, costs and Homer inputs of the wind turbines.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

6.7.5 Grid There are two main grid components when talking about grid extension in Brazil [17, 18]: - The medium voltage lines (MV), in 13.8 kV, used to cover the distance between the existing lines and the users location. - The low voltage lines (LV), in 110 or 220 V, with transformers MV/LV connecting groups of 4 or 5 households to the MV lines with maximum length of 1 km. Figure illustrates the lines distribution.

existing MV line LV MV

grid extension

Figure 46: Diagram of grid extension.

Grid costs are needed both for the simple grid extension and mini-grid options. GOUVELLO and MAIGNE [17] studied 94 projects of rural electrification in Brazil and proposed a method for grid costs definition taking into account both the length cost and economy of scale effect. They did a statistical analysis of the collected data and found that the grid extension cost could be divided in 4 classes according to the number of connected households. The regression analysis used revealed a very good R2 correlation factor as can be seen in figures 47 and 48 .

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Figure 47: Capital cost of grid extension as function of the community size and line length [17].

Figure 48: Capital cost of grid extension as function of the community size and line length (zoom) [17].

Both length cost effect and economy of scale effect can be seen. The economy of scale effect (reduction of the average cost per household when the number of households increases) is represented by a decreasing in the regression line slope when passing from a class of small communities to big communities, for a given length of the connection MV line. The length cost effect is represented by an increase in the average cost per household when the length of the connection MV line increases, for a given class of community size.

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In the present thesis work, we used the given regression line for the class of 14 to 33 households both for simple grid extension and mini-grid options evaluation. In this last one, the MV line length used for calculations was the total length of the MV mini-grid lines. Varying the total MV line length in the mini-grid option we were looking for the mini-grid size that would make the installation of several Home Systems stay in economic advantage. In further real life evaluation of grid extension costs, the minimum necessary information for application of the proposed method, using historical data from a given area or Federal State are: - Number of households connected by each project - Length of the new connection MV line in each project - Project cost In the life cycle cost evaluation of grid extension we made an estimate for the cost of energy coming from the grid based on the tariff to the consumer. This tariff (found in the electricity bills) has 25% of tax so we assumed that the cost of energy would be the tariff price minus 25% of tax minus 10% utility profit. Figures 49 and 50 show the compilation of costs for grid extension to 1 or 30 households.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems Grid extension to 1 household

Source

Capital (installation cost) MV line length km 0.5 1 5 10 15 20 25 30

[17] [17] [17] [17] [17] [17] [17] [17]

grid cost R$ 3,477.00 4,968.00 16,896.00 31,806.00 46,716.00 61,626.00 76,536.00 91,446.00

Electricity cost tarif cost of energy consume Electricity cost

0.45 0.3 40.6 12.18 146.16

R$/kWh R$/kWh kWh/month R$/month R$/year

consumers in BR estimate Sizing 5

Lifecycle cost (installation + cost of energy) MV line length installation cost of energy km R$ R$/year 0.5 3,477.00 146.16 1 4,968.00 146.16 5 16,896.00 146.16 10 31,806.00 146.16 15 46,716.00 146.16 20 61,626.00 146.16 25 76,536.00 146.16 30 91,446.00 146.16 n i

NPC R$ 5,707.00 7,198.00 19,126.00 34,036.00 48,946.00 63,856.00 78,766.00 93,676.00

20 years 3 % / year

cost of grid extension to 1 household 100,000.00

R$

80,000.00 60,000.00 40,000.00

lifecycle cost (NPC) installation cost

20,000.00 0.00 0

5

10

15

20

25

30

MV line length

Figure 49: Grid extension cost for a single household with costs table.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Grid extension to 30 households

Source

Capital (installation cost) MV line length km 0.5 1 5 10 15 20 25 30

[17] [17] [17] [17] [17] [17] [17] [17]

grid cost R$ 3,109.50 3,390.00 5,634.00 8,439.00 11,244.00 14,049.00 16,854.00 19,659.00

Electricity cost tarif cost of energy consume Electricity cost

0.45 0.3 40.6 12.18 146.16

R$/kWh R$/kWh kWh/month R$/month R$/year

consumers in BR estimate Sizing 5

Lifecycle cost (installation + electricity cost) electricity cost MV line length installation km R$ R$/year 0.5 3,109.50 146.16 1 3,390.00 146.16 5 5,634.00 146.16 10 8,439.00 146.16 15 11,244.00 146.16 20 14,049.00 146.16 25 16,854.00 146.16 30 19,659.00 146.16 n i

NPC R$ 5,339.50 5,620.00 7,864.00 10,669.00 13,474.00 16,279.00 19,084.00 21,889.00

20 years 3 % / year

cost of grid extension to 30 households

R$/household

25,000.00 20,000.00 15,000.00 10,000.00

lifecycle cost (NPC) installation cost

5,000.00 0.00 0

5

10

15

20

25

30

MV line length

Figure 50: Grid extension cost for 30 household with costs table. Everson Possamai, Sep/2004

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

6.7.6 Diesel mini-grid The peak load for our village is 7.2 kW. Considering that the village size usually grows after some time, a reasonable gen-set size could be around 10 kW. The smallest size that our gen-set partner Stemac has is 28 kW18 with 30% minimum load that is still too big for our application. Therefore we tried to find the cost of a 10 kW gen-set based on information available in literature plus the quotations for 28 and 54 kW we received. If a regression could be established between power and cost than we could have a well approximated cost for the size of gen-set we needed. The data were taken from references [12], [16], Sol e Vento Energia Alternativa web site and Stemac quotations and plotted in figures 51, 52. Generator Cost 35000 30000

price (R$)

25000 2

y = -9.9961x + 1122.1x + 575.31 2 R = 0.9839

20000 15000 10000 5000 0 0

10

20

30

40

50

60

nominal power (kW)

Figure 51: Generator cost as function of the nominal power (whole range of the study).

Generator Cost 12000 y = 819.34x + 1396.6 2 R = 0.8532

10000

price (R$)

8000 6000 4000 2000 0 0

2

4

6

8

10

12

nominal power (kW)

Figure 52: Generator cost as function of the nominal power (for the range of interest).

18

The nominal power 35 kVA was multiplied by 0.8 due to inductive loads (refrigerator) in order to find the real power.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

These figures show that there is a trend line among the values and thus we can estimate the cost of a 10 kW gen-set with the regression equations. The first one (from figure 51) gives a value of R$ 10,797.00 and the second (from figure 52) gives R$ 9.590 therefore an estimate value of R$ 10,000.00 can be assumed. Other costs and their sources are listed in figure 53 bellow. Note that the mini-grid cost is included in the Homer Diesel gen-set input as a cost sensitivity variable. Doing so, Homer gives a graphic representation of the mini-grid total cost evolution as function of the grid MV length for a given community size. This information will be used latter for decision between several home systems or mini-grid installation.

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Mini-grid costs Power House Capital R$ 10,000.00 3,800.00 13,000.00 1,400.00 600.00 20,000.00 48,800.00

gen set data logger civil works installation start up transformer for voltage elevation

Maintenace (labour + materials) Operation (3 operators in shifts) replacement (only on generator parts)

458.00 0.64 11,400.00 15.83 4,500.00

linear regression Stemac Stemac estimate estimate Stemac

R$/month R$/hr R$/month R$/hr R$

Stemac estimate Stemac

Grid Capital MV line length km 0.5 1 5 10 15 20 25 30

households / km 60.0 30.0 6.0 3.0 2.0 1.5 1.2 1.0

Maintenance MV line length km 0.5 1 5 10 15 20 25 30

R$/month 93.29 101.70 169.02 253.17 337.32 421.47 505.62 589.77

R$ / household 3,109.50 3,390.00 5,634.00 8,439.00 11,244.00 14,049.00 16,854.00 19,659.00

grid cost R$ 93,285.00 101,700.00 169,020.00 253,170.00 337,320.00 421,470.00 505,620.00 589,770.00

R$/hr 0.13 0.14 0.23 0.35 0.47 0.59 0.70 0.82

[17] [17] [17] [17] [17] [17] [17] [17]

estimate estimate estimate estimate estimate estimate estimate estimate

Figure 53: Diesel mini-grid costs.

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Source

Mini-grid Homer inputs

size kW 10

length MV 0.5 1 5 10 15 20 25 30

refference capital cost R$ 142,085.00

replacement R$ 4,500.00

sensitivity factor on refference capital cost 1.00 1.06 1.53 2.13 2.72 3.31 3.90 4.49

refference O&M R$/hr 16.60 sensitivity factor on refference O&M cost 1.00 1.00 1.01 1.01 1.02 1.03 1.03 1.04

sensitivity factor on refference capital cost = (grid cost + power house cost) / refference capital cost sensitivity factor on refference O&M cost = (grid O&M cost + power house maintenance and operation) / refference O&M gnerator life time minimum load ratio consumption intercept consumption slope

16000 hs

Sandia + Stemac

30 %

Stemac Stemac + Homer library Stemac + Homer library

0.1 L/hr / kW 0.235 L/hr / kW

Figure 54: Diesel mini-grid Homer inputs.

6.8 Analysis of options for a single household 6.8.1 Solar Home Systems We started with the design from Sizing 5 as the first reference for further optimisation in Homer. The complete set of inputs and results is given at the end of this section in figure 60 . Then, optimisation process through warnings and sensitivity analysis was done giving a leaner system. A minimum number of 6 batteries was fixed in order to give the 2 days minimum autonomy established by the Light for All program. The final design from both softwares is shown bellow and the main differences marked:

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University of Oldenburg, Germany Analysis of Regional Energy Supply Systems

Tilt angle Autonomy Battery bank DC voltage AC voltage Module power Number of modules BCC specification current Inverter operational power Inverter surge power

Sizing 5 latitude + 15° (47°) 2 days 6 in parallel 12 V 110 V 80 Wp (rated) 75 Wp (operational)19 12 in parallel 58 A 230 W 520 W

Homer 47° 2 days 8 in parallel 12 V 110 V 80 Wp (rated) 75 Wp (operational)20 7 in parallel 35 A 240 W 520 W

The big difference in the number of modules is a result of the design approach. While Sizing 5 uses the worst month as the design month, Homer makes the design for every hour and chooses the best configuration based on a defined energy shortage reference and lowest NPC. The Homer wide range of possibilities for graphical data visualization gives useful information in an easy way to interpret. Figures 55 and 56 show the frequency of battery SOC and its evolution during the 8,760 hours of the year. It is possible to see that the SOC staid bellow 75% only 26% of the time.

Figure 55: Frequency of the battery bank SOC.

Figure 56: Battery SOC hourly plot over the whole year.

19 20

We did not consider derating due to dust, only due to cell operating temperature higher than STC. We did not consider derating due to dust, only due to cell operating temperature higher than STC.

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Figure 57 shows the evolution of all operational parameters: PV power (kW), battery SOC (%), charging/discharging (kW), load profile (kW) and unmet load (kW) for two representative weeks. The first is in November/December, a good month for the SHS operation. With good solar radiation the load is always met and enough energy is directed to the batteries for charging. The same picture shows how the SHS behaves at night, when the load is maximum and the PV power is zero, demanding energy from the battery that was stored during the day.

Figure 57: Plot of the main SHS operational parameters in a good and bad performance week.

The second week is in June, a bad month for the SHS. Low solar radiation keeps the batteries at the minimum allowed SOC (a control that is done by the BCC). The last step in the current design procedure was to check the DIC parameter, if the design done would keep the number of unmet load hours bellow what is established by the Light for All program. DICcalc software was used giving the result shown in figure 58, which is OK.

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OK OK OK OK OK OK OK OK OK OK OK OK OK

DIC

350

hours / month

300 250

DIC due to performance

200

limit

150 100 50 0 J

F

M

A

M

J

J

A

S

O

N

D

year

Standards DIC: days for maintenance DIC due to performance LOLP due to performance LOLP total

per month 9 days 5 days 96 hours 13% 30%

per year 27 days 15 days 288 hours 3% 7%

Figure 58: DICcalc result for DIC evaluation of the SHS.

Looking to the costs, figure 59 gives the lifecycle and installation costs of the system expressed in annuities (a pizza graphic of the NPC would be the same).

lifecycle cost

installation cost

Converter 31%

Converter 35% PV Array 49% PV Array 60%

Battery 9%

Battery 16%

PV Array 16,460.00 R$ Battery 2,400.00 R$ Converter 8,510.00 R$ Total 27,370.00 R$

PV Array Battery Converter Total

1,256.00 R$ / year 431.00 R$ / year 928.00 R$ / year 2,615.00 R$ / year

Figure 59: Case study SHS costs with components share.

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TOTAL

70% AC load

generator derate BCC

installed power W SUMMER 240 MIDLE S 240 WINTER 240

inverter

100%

90%

80% battery (Wh effi.) wiring Impp (STC) Vmpp (STC) Voc (STC) Isc (STC) module operating T load voltage

4.57 17.5 21.8 4.85 40 110.0

A V V A °C V

minimun allowed autonomy max battery DOD of the selected battery (at 25*C, 20h) battery lowest ambient temperature nominal DC system voltage nominal battery voltage peak current

2 60% 105.0 15 12 12 20.0

days

design current Batteries minimun allowed autonomy battery derating factor required battery capacity batteries in parallel batteries in series total batteries system battery capacity usable battery capacity PV Array derated array design current operational Impp modules in parallel voltage required to charge batteries operational Vmpp modules in series total modules array current array short circuit current array voltage array open circuit voltage

522

L + 15 47 5.15 4.88 4.78 4.41 3.88 2.96 3.41 3.98 4.16 4.98 5.22 5.39

corrected Ah/day 158.4 161.1 165.9

design current A 30.75 32.45 33.12 36.51 41.50 54.35 48.63 41.71 39.90 32.37 30.86 29.87

max 0.00 0.00 0.00 0.00 0.00 54.35 0.00 0.00 0.00 0.00 0.00 0.00

97%

-72.5 mV/°C -72.5 mV/°C 0.8 mA/°C

Ah °C V V A

corrected Ah/day J F M A M J J A S O N D

surge

Ah load Ah/day 122.9 125.0 128.8

design current A 25.20 27.83 30.15 36.18 44.33 58.03 52.37 42.48 37.20 28.46 25.59 23.97

L - 15 17 6.28 5.69 5.25 4.45 3.63 2.78 3.17 3.91 4.46 5.66 6.29 6.72

158.36 158.36 158.36 161.09 161.09 161.09 165.92 165.92 165.92 161.09 161.09 161.09

max 0.00 0.00 0.00 0.00 0.00 58.03 0.00 0.00 0.00 0.00 0.00 0.00

L 32 6.22 5.46 4.78 3.79 2.94 2.28 2.56 3.26 4.00 5.30 6.16 6.71

design current A 25.45 29.00 33.12 42.50 54.79 70.53 64.71 50.83 41.50 30.37 26.16 24.01

max 0.00 0.00 0.00 0.00 0.00 70.53 0.00 0.00 0.00 0.00 0.00 0.00

Impp Vmpp Pmpp Isc Voc

STC 4.6 17.5 80.0 4.9 21.8

operation 4.6 16.4 75.2 4.9 20.7

min 0.00 0.00 0.00 0.00 0.00 54.35 0.00 0.00 0.00 0.00 0.00 0.00

54.3 A

2.0 1.0 582.2 6.0 1.0 6.0 630.0 378.0

54.3 4.6 12 14.4 16.4 1 12 operational 55.0 58.3 16.4 20.7

days Ah

Ah Ah

A A V V

rated 54.8 58.2 17.5 21.8

A V Wp A V

derating factor due to T total derating factor

94% 94%

A A V V

BCC operational

rated

Figure 60: Screen of Sizing 5 for the case study design.

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6.8.1.1 Design results summary SHS Tilt angle Autonomy Battery bank DC voltage AC voltage Module power Number of modules BCC specification current Used BCC Inverter operational power Inverter surge power Installation Cost NPC Cost of Energy

47° minimum 2 days 8 un in parallel 12 V 110 V 80 Wp (rated) 75 Wp (operational) 7 in parallel 35 A 45 A with data-logger 240 W 520 W R$ 27,370.00 R$ 38,899.00 R$ 5.06 / kWh

6.8.2 Wind Home Systems In this design, we did not need to make a pre-design like in the SHS due to the reasons already mentioned in section. As it was done in the SHS design, we fixed the minimum number of batteries in 6 because of the required autonomy. First trial was done with the smallest size (Gerar 164). Homer asked for more than 10 batteries so we moved the attention to the other model (Gerar 210). The final configuration was a system with 6 batteries. The turbine DC voltage is 24 V so these 6 batteries have to be assembled 2 in series x 3 in parallel. Since the maximum current through the BCC (1,000 W / 24 V = 42 A) is bellow 45 A and the load characteristics are the same as for the SHS we used the same converter for both WHS and SHS. Regarding the battery behaviour, we can see the difference between a WHS and a SHS comparing figures 57 and 61. With a much stronger variation in the energy generation, the WHS has a more unstable behaviour of the SOC. But despite of that, the WHS shows a surprisingly more reliable operation than the SHS (see DIC graph in figures 63 and 58). This is because the battery bank is larger than it was really needed. We did another trial in Homer removing the limitation of 6 batteries and found that 4 would be the best option.

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Figure 61: Plot of the main WHS operational parameters in a good and bad performance week.

The same effect is responsible for the low frequency of SOC bellow 75%: only in 17% of the time (see figure 62 ).

Figure 62: Battery SOC hourly plot over the whole year.

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DIC due to performance J 0 F 0 M 6 A 3 M 0 J 0 J 0 A 20 S 0 O 0 N 0 D 10 year 39

OK OK OK OK OK OK OK OK OK OK OK OK OK

DIC

350

hours / month

300 250

DIC due to performance

200

limit

150 100 50 0 J

F

M

A

M

J

J

A

S

O

N

D

year

Standards DIC: days for maintenance DIC due to performance LOLP due to performance LOLP total

per month 9 days 5 days 96 hours 13% 30%

per year 27 days 15 days 288 hours 3% 7%

Figure 63: DICcalc result for DIC evaluation of the WHS.

Figure 64 shows the installation and lifecycle costs (in annuities) of the WHS.

lifecycle cost

installation cost Wind turbine 25%

Wind turbine 27%

Converter 50%

Converter 59%

Battery 16%

Wind turbine 3,680.00 Battery 2,400.00 Converter 8,510.00 Total 14,590.00

R$ R$ R$ R$

Battery 23%

497.00 R$ / year 431.00 R$ / year 928.00 R$ / year 1,855.00 R$ / year

Figure 64: Case study WHS costs with components share.

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6.8.2.1 Design results summary WHS Rotor diameter Hub height Rated power Annual production 24h/day at rated power Simulated annual production production)

2.1 m 8.5 m 700 W 6,132 kWh 1,157 kWh (19% of the full time rated

Autonomy Battery bank DC voltage AC voltage Number of modules BCC specification current Used BCC Inverter operational power Inverter surge power

minimum 2 days 2 in series and 3 in parallel 24 V 110 V 6 in parallel 42 A 45 A with data-logger 240 W 520 W

Installation Cost NPC Cost of Energy

R$ 14,590.00 R$ 27,600.00 R$ 3.55 / kWh

6.8.3 Evaluation of the best option for a single household The NPC and installation costs of the three analysed options are shown in figure 65.

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R$

40,000.00 35,000.00

grid NPC

30,000.00 25,000.00 20,000.00

grid extension

15,000.00 10,000.00

WHS NPC

SHS NPC SHS installation WHS installation

5,000.00 0.00 0

2.5

5

7.5

10

12.5

15

MV line length

Figure 65: Graphic with costs of all electrification options for a single household in the case study.

The first remarkable result is that the break-even point between stand-alone systems and grid extension is not at the installation costs but at the lifecycle costs. In our case study, the grid extension is the cheapest option for distances bellow 8 km (the break even point at the lifecycle cost lines). If one is looking only to the installation cost, this distance would be already 4 km having the wrong conclusion that the grid extension would be cheaper only for distances up to 4 km. The second important result is that the main responsible for this difference is not the grid lifecycle cost but the standalone life cycle cost. The difference between the installation and lifecycle lines of the grid is much smaller than that of the stand-alone system. In figure 65, the error if one would consider grid lifecycle cost equal to installation cost is about 500m. But if one would neglect both grid and stand-alone lifecycle costs the error would be 4.5 km (9 times more). Comparing WHS with SHS in our case study, the WHS is in clear advantage. The SHS would be cheaper than the grid only for distances over 11 km. Since the costs were looking to high, we tried to find references in the literature about cost of energy for stand alone systems. We found two for SHS in Europe [19], [20] whose values are: - SHS cost of energy: 1.9 - 2.6 US$/kWh (6 - 8 R$/kWh) Just directly looking to the values, our SHS does not look bad (5.06 R$/kWh). It is even better if we take in account that the components are cheaper in Europe than in Brazil, due to the importation taxes, freight and resale commission. We were reported that, in some cases, these costs can make the equipment have a price 2.9 times higher in

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Brazil than in Europe. Another example is the PV modules we used: they are 40% more expensive in Brazil than in Germany21. It is very important to point out that the stand-alone systems we designed are not the most common ones in developing countries. The need of data logging and supply only AC loads as well as the use of a refrigerator in the household increased considerably the cost of the systems.

6.9 Analysis of options for a community 6.9.1 Diesel mini-grid Since gen-set manufacturers usually specify a minimum load to their product, there is generated energy to be dumped in resistors. Figure 66 shows the performance of our system considering this loss when the minimum load is 30% of the rated capacity.

Figure 66: Diesel mini-grid performance in a typical day.

To evaluate this effect and others over the lifecycle cost, we did sensitivity analysis on the following inputs: - Minimum load on the gen-set, to see the impact on costs if a substantial reduction on the dumped energy is done. - Installation and O&M costs, representing the system cost evolution with increasing mini-grid length. This is the same as studying the change in costs when the load per MV line length changes. - Diesel price, to evaluate the effect of a 100% Diesel price increase on the system lifetime cost.

21

We assumed the cost in Germany as 5 € / Wp.

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6.9.1.1 Minimum load study This input was changed from 0% (no restriction at all) to 30% of the nominal power (the usual value). The NPC changed only 5% (see figure 67) and the COE changed 4%, thus this change can be neglected in the next sensitivity analysis and the value of 30% used as suggested by the manufacturers.

Figure 67: Effect of the minimum load parameter on the Diesel mini-grid NPC.

6.9.1.2 Load per line length and fuel cost study

Figure 68 show the simulation results with the sensitivity analysis on mini-grid length and Diesel price. We can see that the fuel price increase has only moderate impact on the NPC. Analysing figure 69 we can see that even if the fuel cost increases 100%, the total change in the lifecycle cost is just 10% due to the fact that the fuel participation on the total cost is just maximum 19%. Operation and maintenance play the major role.

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R$

Millions

Diesel mini-grid NPC as function of load/km and fuel price 4 3.5 3 2.5 2 1.5

1.5 R$/l

1 0.5 0

3 R$/l

0

10

20

30

40

50

60

households/km

Figure 68: Graphic with both fuel price and load/length effects on the Diesel mini-grid NPC. lifecycle cost (Diesel price R$ 1.50 / l)

fuel 11%

lifecycle cost (Diesel price R$ 3.00 / l)

capital 20%

capital 18%

fuel 19%

replacement 1%

replacement 1%

O&M 68%

O&M 62%

fuel price 3 1.5 R$/l capital 42,881.00 42,881.00 R$/year replacement 2,266.00 2,266.00 R$/year O&M 151,233.00 151,233.00 R$/year fuel 46,650.00 23,325.00 R$/year

Figure 69: Diesel mini-grid costs and cost categories share.

The number of households/km was the variable used to study the effect of increasing load on the lifecycle cost, obtained dividing our community size of 30 households by the MV line total length being analysed. The aim was to find the load/km that would make the mini-grid cheaper than several home systems, which is done in section 6.9.3.

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6.9.2 Several Home Systems The life cycle cost for 30 SHS or WHS is just a factor of 30 over the single one cost: SHS R$ 1,166,970.00

NPC

WHS R$ 828,000.00

6.9.3 Evaluation of the best option for a community Once the individual life cycle costs of all options are know, it is possible to plot them together and look for the break-even points. This is done in figures 70 and 71 .

Millions

system NPC 4 3.5 3

R$

2.5 2 1.5 1 0.5 0 0

10

20

30

40

50

60

70

80

MV line length mini-grid (Diesel @ 1.5 R$/l) mini-grid (Diesel @ 3 R$/l) 30 SHS 30 WHS grid extension

Figure 70: Graphic with costs of all electrification options for the 30 households in the case study (NPC as function of the line length).

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Millions

system NPC

4 3.5 3

R$

2.5 2 1.5 1 0.5 0 0

1

2

households/km mini-grid (Diesel @ 1.5 R$/l) mini-grid (Diesel @ 3 R$/l) 30 SHS 30 WHS grid extension

Figure 71: Graphic with costs of all electrification options for the 30 households in the case study (NPC as function of the load/length).

The grid extension is the cheapest option for communities with an average load over 0.75 households / km (or a 40 km connection line to our 30 households). For loads bellow this point, the first option would be the electrification with WHS. A careful evaluation of visual and environmental impacts of the 30 wind turbines has to be considered prior to the final decision. The next option are the SHS. They are cheaper than grid extension over 0.5 households / km (or a 60 km connection line for our 30 households). The Diesel mini-grid for this case study revealed to be the most expensive option with no break-even point between the other options. The main cost factor for this behaviour is not the fuel cost but the O&M cost.

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7 Conclusions about the proposed strategy We demonstrated the main results that can be achieved with this proposed evaluation strategy. The potential of the softwares used goes beyond what was presented. Many other combinations of components can be done and also evaluated with other available graphical features. It was demonstrated that useful detailed information can be extracted using tools easily available. Besides the fact that very short times of processing and design are needed, a more time consuming step has to be done looking for the right information to be given as input. During the development of the work we found that information about PV power and conditioning components are easily available in literature and in the market but the same is not true for batteries and distribution grid. In their respective chapters we gave support information in order to make the information search easier for the strategy user. Regarding the case study inputs and results, the WHS and SHS have real case design status. Our partner companies supplied us with updated real market quotations plus application know-how. In the case of mini-grid and distribution grid the level of input assumptions was high but supported by information given by our partners and literature. Demonstration results from the processing of this information were done in order to show what can be expected in real life. Since the objective of the thesis work was not to make a design for a specific case but rather define an evaluation strategy, we left the structure defined so real case study data can be inserted and results understood during the application of the strategy.

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8 References [1] [2] [3] [4] [5]

[6] [7]

[8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

Federal Government of Brazil (2003). "Presidential Decree 4873 of 11th November 2003". Download at https://www.planalto.gov.br/ccivil_03/decreto/principal_ano.htm. SANTOS, Rosana Rodrigues dos (2004). Presentation given in the PPRE Summer School in Oldenburg – Germany in June 2004. MME Brazilian Ministry of Mines and Energy (2004). "Cartilha Luz para Todos". Download at www.mme.gov.br/luzparatodos. UNDP United Nations Development Program; IPEA Applied Research Institute; Fundacao Joao Pinheiro (2000). "Human Development Atlas of Brazil". MME Brazilian Ministry of Mines and Energy (2004). "Operational Manual of the Light for All Program". Download at http://www.mme.gov.br/luzparatodos/download/mme_manual_operacionalizacao. pdf ANEEL National Electric Energy Agency of Brazil (2003). "Resolution number 223". Download at www.aneel.gov.br. ANEEL National Electric Energy Agency of Brazil (2004). "Technical Paper 012/2004 – SRC/SRD/SRG/SIH". Donwload at http://www.aneel.gov.br/aplicacoes/Audiencia_Publica/audiencia_proton/2004/au di012.htm SEMC Secretary of Mining, Energy and Communications of Rio Grande do Sul (2002). "Wind Atlas of Rio Grande do Sul". COLLE, Sergio; PEREIRA, Enio Bueno (1998). "Solar Radiation Atlas of Brazil". INMET INPE and LABSOLAR UFSC. SANDIA National Laboratories (1995). "Stand Alone Photovoltaic Systems: A Handbook of Recommended Design Practices". Download at www.sandia.gov. SANTOS, Rosana Rodrigues dos (2002). "Procedimentos para Eletrificacao Rural Fotovoltaica Domiciliar no Brasil: uma contribuicao apartir de observacoes de campo" PhD Thesis. PIPGE - USP. OCACIA, Gilnei Carvalho; SANTOS, Joao Carlos Vernetti; MARANGHELLO, Moacir; LIBERMAN, Bernardo (2002). "Utilizacao de Grupos Geradores a Gasolina para Eletrificacao Rural". Paper presented in the Agrener 2002 event. OCACIA, Gilnei Carvalho; SANTOS, Joao Carlos Vernetti; CONSUL, Renato de Avila (2002)."Sistemas Fotovoltaicos e Sistemas Hybridos para Eletrificacao Residencial Rural". Paper presented in the Agrener 2002 event. OCACIA, Gilnei Carvalho (1998). "Utilizacao da Energia Eolica na Planicie Costeira do Rio Grande do Sul". PROMEC-UFRGS. LINDEN, David; REDDY, Thomas B. (2002). "Handbook of Batteries". McGraw-Hill. MESSENGER, Roger; VENTRE, Jerry (2004). "Photovoltaic Systems Engineering". CRC Press LCC. GOUVELLO, Christophe; MAIGNE, Yves (2003). "Eletrificacao Rural Descentralizada". CRESESB - CEPEL and Systemes Solaires. COSTA, Heitor S.; DA COSTA, Rodrigo A.; ECK, Myriam (2000). "Analise comparativa entre a extensao da rede e os sistemas fotovoltaicos". Revista Eletricidade Moderna, June 2000.

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[19] HOLTORF, Hans (2004). PPRE course teaching material. University of Oldenburg, Germany. [20] LOOIS, Geerling; VAN HEMERT, Bernard (1999). "Stand alone photovoltaic applications, lessons learned". IEA International Energy Agency.

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9 Appendices

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9.1 Interest rates paid by bank investments

L

H

N

N: no risk L: low risk* H: high risk* *: 20% have to be reduced from the informed interest rates due to income tax Everson Possamai, Sep/2004

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9.2 Names and addresses of the participant companies STEMAC S/A GRUPOS GERADORES Av. Sertório 905 91020-001 Porto Alegre - RS - Brazil Telephone: 00 55 51 3358 3877 Web page: www.stemac.com.br Contact person: Pedro Augusto Büttenbender e-mail: [email protected] IEM Intercambio Eletro Mecanico Ltda. Av Amazonas 800 90240-541 Porto Alegre - RS - Brazil Telephone: 00 55 51 3343 4455 Web page: www.iem.com.br Contact person: Hans Dieter Rahn e-mail: [email protected] Enersud Industria e Solucoes Energeticas Ltda R. Brasilina Rosa de Jesus 02 office 201 24750-690 Tribobó - São Gonçalo - RJ - Brazil Telephone: 00 55 21 3710 0896 Web page: www.enersud.com.br Contact person: Bruno Bressan De Cnop e-mail: [email protected]

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9.3 CV of the author Curriculum Vitae Everson Possamai Experience in team management, project engineering and implementation/execution, process engineering, waste and environmental management. Experience in national and multinational companies. Graduated in Mechanical Engineering, course of Environmental Management in Germany, Master Degree in Renewable Energy (University of Oldenburg - Germany). Fluent in English, good level of Spanish, basic German, 33 years old. Address: Travessa Jaguarao 77 apto 602 CEP 90520-070, Porto Alegre Brasil e-mail: [email protected]

Education

Federal University of Rio Grande do Sul - Ufrgs , Brazil. B.Sc. in Mechanical Engineering. Graduation Work title: "Design of a Bicycle Frame Using Finite Element Analysis". January 1995. MVV Energie AG, Germany. Environmental Management. Final Work title: "Recycling and Recovery of Post-consumer Plastics in Germany: Situation and Possibilities for Brazil". July 1999. University of Oldenburg, Germany. Master Degree in Renewable Energy. Thesis Work title: "Strategy for Analysis of Regional Energy Supply Systems". September 2004.

Advanced Training

Landfill site project, start-up and operation, 24hrs. Industrial Methods and Processes, 14hrs. Pumps selection, 32hrs. Steam distribution, 16hrs. Steam piping design project, 40hrs. Costs reduction and productivity, 16hrs. Management tools for chiefs and supervisors, 16hrs. Meetings Management, 12hrs. Management ability development, 16hrs. Industrial Methods and Process Evaluation, 14hs. Finite Element Analysis, 15hrs. Painting surface treatment techniques, 16hrs. Instruments of Environmental Management, 45hs.

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Pet Products LTDA/Hartz Group USA December 2000 to July 2003. Project and Process Engineer. Responsible for the Waste Water Treatment Station project execution (management of a US 1.335.000,00 project). Responsibility over the project and execution of new production lines. Development of drying techniques for food drying processes. Consultant for Environmental Management and Waste Management. August 1999 to December 2000. Projects for Waste treatment and recycling. Training in Environmental Management and Waste Management. Technical and economical feasibility study for a biogas plant. Kepler Weber S.A., Brazil. April 1998 to January 1999. Project Engineer. Responsible for various projects of complete feed mill plant. Work as Sales Engineer. Projects worked on included feed mill plants from 2ton/h to 160ton/h from the receiving to expedition with building, electrical installations and utilities. Effem do Brasil Inc. & Cia/Mars Group USA, Brazil. November 1995 to April 1998. Responsible for the Environmental Management. Responsible for the process engineering of pet food production Effem/Mars do Brasil Inc. & Cia/Mars Group USA, Brazil. January 1995 to November 1995. Worked as Shift Supervisor at the canning plant. Responsible for the production and team management. Effem/Mars do Brasil Inc. & Cia/Mars Group USA, Brazil. January 1994 to January 1995. Worked as trainee at the Industrial Engineering Department. Responsible for production efficiency studies.

Research Experience

Federal University of Rio Grande do Sul - UFRGS, Brazil. April 1989 to February 1993. Worked as a Research Trainee. Undertook projects on Powder Metallurgy and mass flow meters development.

Computer Skills

Computer: CAD/CAE: AUTOCAD, ANSYS, ALGOR Office Data Processing: Microsoft Office Graphic Editor: Corel Draw Internet: Outlook Express, Netscape Navigator, Internet Explorer Simulation: Homer

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Family Status: Married and one daughter 2 years old. Brazilian and Italian Citizenship. English level: TOEFL 580 and Cambridge University Proficiency Test FCE.

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9.4 Final Mark and Certificate

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