CHAPTER 4 TECHNO-ECONOMIC ANALYSIS OF ISOLATED AND GRID CONNECTED HYBRID SYSTEMS

Transcription

1 CHAPTER 4 TECHNO-ECONOMIC ANALYSIS OF ISOLATED AND GRID CONNECTED HYBRID SYSTEMS 4.1 INTRODUCTION The greatest threat to the reliable power system is keeping all generators in synchronism by fulfilling the load demand at all times. As the load demand is ever increasing and unavoidable part of any power system, hence, a well organized power system must be provided to enhance the system performance by including the generation with renewable energy sources. The inclusion of renewable energy sources can meet the increasing load demand either by islanded or grid connected operation. One of the most recognized terms in today s electricity market is deregulation. To realize the potential of distribution generation, generation and load must be taken as a subsystem. This system may use any combination of generation, load and storage technologies and can operate in grid connected mode or autonomous mode. Some examples of micropower system or microgrid are solarbattery serving a remote load, wind-diesel system serving an isolated village, a grid connected natural gas microturbine providing heat to a factory. Micropower system consists of electric and thermal load, and any combination of photovoltaic modules (PV), wind turbine, small hydro, biomass power generation, microturbines, fuel cells, reciprocating engine generators, batteries and hydrogen storage. The analysis and design of micropower system is challenging due to large number of design options and uncertainty in key parameters such as load size and future fuel price. Renewable energy sources add further complexity because the output may be intermittent, seasonal and nondispatchable and the availability is uncertain. Penetration of DG across the country has yet not reached the significant levels. These emerging technologies have lower emission and potential to have lower cost. A better way to realize the potential of distributed generation is to take a system approach where load and generation acts as a subsystem called microgrid. This is a decentralized and bidirectional pattern permits electricity import from the grid and electricity export to the grid. A plant that produces electricity less than

2 kw comes under micro generation technologies. Microgrid sources can produce electrical energy and thermal energy both. Hence, the penetration of distributed energy resources both at low voltages and medium voltages (LV and MV) in utility and downstream networks have been increased in developed countries like USA, Canada, Japan. Currently, microgrid technologies are being popularly used for Indian power sector by employing PV, Wind, Fuelcell, Biomass and different energy storage techniques. Use of Techno-Economic analysis and feasibility study of a new site, as suggested in [92], [142], was a revolutionary step to solve the site selection for better performance of microgrid. In this chapter, Techno-Economic analysis is used as a powerful analyzing tool for checking feasibility of a site for implementing a microgrid. For this work, a site Charanka, Kutch, is selected for feasibility investigation of a microgrid with different configurations. The site location of the site used is latitude and longitude obtained from NASA website [51]. This work is a technical and economic feasibility study of a hybrid generating system, composed of PV, wind, diesel and grid resources feeding a customer with high reliability requirements of electric supply. The chapter begins with current state of the art in feasibility study of microgrid, role of HOMER software for modeling of system and resource generation algorithm. After that, two cases (i) Wind-Diesel and (ii) PV-Wind-Diesel system have been analyzed and simulated in isolated and grid connected modes and results have been explained in details. 4.2 CURRENT STATE OF THE ART FOR MICROGRID FEASIBILITY Microgrids have revolutionized the scenario of power system. Functional integration with the grid, islanded operation, meeting the load requirements, reliability of the main grid and reducing the GHG emissions are few of the benefits of this revolution. Many microgrid configurations have been proposed by different researchers for different sites. G.Bekele and G. Tadesse [38] presented a Feasibility study of small Hydro/PV/Wind hybrid system for off-grid rural electrification in Ethiopia. Although the proposed system has a relatively higher COE than the national tariff, in view of the energy shortage at the national level, resistance to deforestation, clean energy 83

3 development, changing the life of the poor in remote regions and expansion of power generation it is a highly commendable solution. M. da Rocha and his colleagues [92] proposed feasibility study for hybrid electric generating system with Wind-Diesel and grid sources for feeding the energy demand with more reliability and keeping or improving the clean characteristics of the Brazilian electrical matrix. The analysis of economic viability showed to the attractiveness of the project, providing a reasonable payback time. M. Dowling and others [93] presented economic feasibility analysis of electricity generation from landfill gas in South Africa using Municipal Solid Waste (MSW). Methane collection from LFGTE is a green house gas that contributes to the global warming twenty one times more than carbon dioxide. S. Rehman and his coworkers [142] presented Feasibility study of a wind-pv-diesel hybrid power system for a village. The possibility of utilizing power of the wind and sun to reduce the dependence on fossil fuel for power generation to meet the energy requirement of a small village Rowdat Ben Habbas located in the north eastern part of Saudi Arabia is checked. D. Saheb-Koussa and his team [29] proposed an economic and technical study of a hybrid system (wind photovoltaic diesel) for rural electrification in Algeria. The primary objective of this study is to estimate the appropriate dimension of stand-alone hybrid photovoltaic/wind/diesel with battery storage that guarantee the energy autonomy of typical remote consumer with lowest cost of energy. J. Dekker and his team [65] presented an economic analysis of PV/diesel hybrid power systems in different climatic zones of South Africa. Rural communities in South Africa endure poor access to electricity mostly due to the lack of grid connected power lines. K.R. Ajao and others [79] proposed cost benefit analysis of hybrid solar-wind generation relative to utility in Nigeria. Richard W. Wies et. al. [129] presented a Simulink Model for economic analysis and environmental impacts of a PV with Diesel-Battery system for remote villages. Although, numbers of feasibility schemes have been proposed so far, there exists a lot of scope for further improvement especially in technical, economical and environmental impacts of renewable sources in autonomous and grid connected modes for a location in India. In order to overcome most of the drawbacks of the above schemes, three novel techniques for microgrid analysis have been proposed using techno-economic analysis. Also, the environmental impacts of these configurations have been simulated and analyzed. 84

4 (i) Initially, feasibility of the proposed algorithm have been tested on a Wind- Diesel hybrid system proposed for a site in Kutch, Gujarat, India. (ii) In order to further improve microgrid performance, a scheme with PV- Wind- Diesel is developed for feasibility study. (iii) Eventually, the extension of the approach described in (i) and (ii) is utilized for the development of a new grid connected microgrid. 4.3 SOFTWARE SELECTION FOR MODELING OF THE SYSTEM The software used for this purpose is HOMER (Hybrid Optimization Model for Energy Resources). This software has the capability of performing the analysis in small time steps. HOMER, the micro power optimization model, simplifies the task of evaluating designs of both off-grid and grid-connected power systems for a variety of applications. When you design a power system, you must make many decisions about the configuration of the system: What components does it make sense to include in the system design? How many and what size of each component should you use? The large number of technology options and the variation in technology costs and availability of energy resources make these decisions difficult. HOMER's optimization and sensitivity analysis algorithms make it easier to evaluate the many possible system configurations. It enables the user to schematically construct a configuration of hybrid power system, include number of technology options, run a simulation, analyze the results, manage the data in a completely integrated and graphical environmental [53]. Online plotting functions, controls and resources are also included in HOMER so that the user can alter system parameters. HOMER simulates the operation of a system by making energy balance calculations in each time step of the year. For each time step, HOMER compares the electric and thermal demand in that time step to the energy that the system can supply in that time step, 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 in each time step how to operate the generators and whether to charge or discharge the batteries. It performs these energy balance calculations for each system configuration that you want to consider. It then determines whether a configuration is feasible, i.e., whether it can meet the electric demand under the conditions that you 85

5 specify, and estimates the cost of installing and operating the system over the lifetime of the project. The system cost calculations account for costs such as capital, replacement, operation and maintenance, fuel, and interest. 1) Optimization After simulating all of the possible system configurations, HOMER displays a list of configurations, sorted by net present cost (sometimes called lifecycle cost), that you can use to compare system design options. 2) Sensitivity Analysis When you define sensitivity variables as inputs, HOMER repeats the optimization process for each sensitivity variable that you specify. For example, if you define wind speed as a sensitivity variable, HOMER will simulate system configurations for the range of wind speeds that you specify. 4.4 RESOURCE GENERATION ALGORITHM For the techno-economic analysis, the microgrid has been represented using the microsources, convertor, battery and grid in HOMER. To ensure the proper operation of microgrid, resource data must be obtained from the reliable source. Here, PV and Wind resource data are obtained using different algorithms and websites. Latitude The latitude specifies your location on the Earth's surface. It is an important variable in solar calculations. This location is used to calculate radiation values from clearness indices, and vice versa. It also uses the latitude to calculate the radiation incident on a tilted surface. Baseline Data PV Resource The baseline data is a one-year time series representing the average global solar radiation on the horizontal surface, expressed in kwh/m 2, for each time step of the year. HOMER displays the monthly average radiation and clearness index of the 86

6 baseline data in the solar resource table and graph. There are two ways to create baseline data: you can use HOMER to synthesize hourly data from monthly averages, or you can import time series radiation data from a file. To synthesize data, you must enter twelve average monthly values of either solar radiation or clearness index. You do not have to enter both; it calculates one from the other using the latitude. Enter each monthly value in the appropriate row and column of the solar resource table. As you enter values in the table, it builds a set of 8,760 solar radiation values, or one for each hour of the year. The proposed algorithm creates the synthesized values using the Graham algorithm [135], which results in a data sequence that has realistic day-today and hour-to-hour variability and autocorrelation. A stochastic procedure for generating synthetic sets of hourly solar irradiation values, suitable for use in solar simulation design work. The daily atmospheric transmittance Kt for the day is broken down into hourly irradiation events by a stochastic procedure. Random fluctuations are ignored in all solar study, but research by Graham has shown the thermal performance of a number of solar energy devices. To build a model, the effect of process history, air mass and daily clearness index have been investigated. The solar radiation data for the proposed site is obtained from NASA's Surface Solar Energy Data Set provides monthly average solar radiation data for everywhere on earth at For this work, solar radiation data is obtained from NASA website. The latitude and longitude for the selected site is and Wind Resource When you have no access to measured wind speed data, you can create time series wind speed data using HOMER's synthetic wind speed data synthesis algorithm. This algorithm requires you to enter a few parameters, from which it generates artificial but statistically reasonable time series data. The algorithm produces data that mimic the characteristics of real wind speed data, including strong and sustained gusts, long lulls between windy periods, and seasonal and diurnal patterns. 87

7 Parameters To generate synthetic wind speed data, go to the Wind Resources Inputs window and choose Enter monthly averages. You must enter the twelve monthly average wind speeds, as well as the following four parameters: Parameter Weibull k 1-hour autocorrelation factor Diurnal Pattern Strength Hour of Peak Wind Speed Description Reflects the breadth of the distribution of wind speeds over the year Reflects how strongly the wind speed in one time step tends to depend on the wind speed in the previous time step. Reflects how strongly the wind speed depends on the time of day. Reflects how strongly the wind speed depends on the time of day. Table 4.1 Parameters for generating synthetic wind speed data One can estimate the value of each of these parameters without detailed knowledge of the wind data in a particular location. The articles on each of the parameters give guidance for doing so. Algorithm HOMER follows a five-step process to synthesize one year of time series wind speed data: Step 1 In the first step of the algorithm, HOMER generates a sequence of auto correlated numbers, one for each time step of the year, using the first-order autoregressive model: Z t = a. Z t 1 + f(t)...(4.1) Where 88

8 Z t = The value in time step i Z t-1 = The value in time step i-1 a = the autoregressive parameter f(t) = a white noise function that returns a random number drawn from a normal distribution with mean of zero and standard deviation of 1. HOMER sets the autoregressive parameter equal to the one-time-step autocorrelation coefficient: a = r 1 (4.2) But on the Wind Resource Inputs window you enter the one-hour autocorrelation coefficient, which is different from the one-time-step autocorrelation coefficient if the time step is not 60 minutes. To calculate the one-time-step autocorrelation coefficient from the one-hour autocorrelation factor, HOMER assumes logarithmic decay in the autocorrelation function, in which case the following equation gives the autocorrelation parameter for a lag of k time steps: r k = r 1 k (4.3) Solving that for r 1 gives: r 1 = exp lnr k k (4.4) The one-hour autocorrelation factor is r k where k is the number of time steps that fit in one hour, meaning: k = 60 t (4.5) Where t is the time step in minutes. This first step of the algorithm produces a series of numbers that conform to a normal distribution with a mean of zero and a standard deviation of 1. 89

9 Step 2 In the second step of the algorithm, HOMER creates a full year of data by piecing together the desired average diurnal wind speed profile, repeated every day. Because the average wind speed varies by month, the average diurnal wind speed profile scales to a different value each month, but within each month the diurnal pattern simply repeats over and over. Step 3 In the third step, HOMER performs a probability transformation on the sequence of numbers generated in Step 2 so that it conforms to the same normal distribution as the sequence generated in Step 1. Step 4 In the fourth step, HOMER adds the sequence generated in Step 3 to the sequence generated in Step 1. The resulting sequence conforms to a normal distribution, but exhibits the desired degree of autocorrelation. Step 5 In the fifth and final step, HOMER performs a probability transformation on the sequence generated in Step 4 to make it conform to the desired Weibull distribution. 4.5 PROPOSED ALGORITHM FOR TECHNO-ECONOMIC ANALYSIS Modeling of microgrid configuration and the proposed algorithm scheme are discussed in this section Proposed Algorithm Two different cases of hybrid system are taken into consideration (i) Wind- Diesel Hybrid System and (ii) PV-Wind-Diesel Hybrid System. This section explains the detailed algorithm steps to build the schematic and carry out techno-economic analysis. 90

10 1) Select different types of generators depending upon the availability of resources. Prior survey of site for available resources, load and existing generation is required for this purpose. 2) Decide maximum generation capacity for each type of generation. The attributes to be taken into account for this purpose can be reliability of Microgrid and operating reserve. Power exchange with grid can be additional attribute for energy deficit country. 3) Select incremental step size for each generator which is available commercially and generally installed. For example, biomass gasifier systems are commercially available in the range of 500 kw to few MW. Hence, incremental step size can be set to 500 kw for biomass fuelled generators. 4) Give priority to the DGs, i.e., from where the power should come first. For example, natural resources will be on higher priority as compared to fossil fuel based DGs. 5) Generate all possible combinations for selected generators, ranging from zero to maximum possible installation of each DG. 6) Check the generated combinations for validity. Each valid combination has to satisfy system s electrical as well as thermal load requirement. 7) Calculate NPC and COE for each valid combination. 8) Find minimum of all NPC values and index corresponding to the minimum NPC. 9) The combination corresponding to minimum NPC is the optimal mix of the DGs. Constraints on the objective function: 1) The output of each generator must be always positive, i.e., Pgi 0. 2) Maximum generation limit of renewable energy resources is limited by expected power selling, amount of reserve capacity, and availability of natural resources. Maximum rating of fuelled generator should be such that, total load of MicroGrid can be supplied irrespective of other types of DGs. This maximum limit is defined as Pg Pgmaxi. 3) The amount power exchanged between DG and utility is restricted by a mutual contract and Government regulations. According to Electricity Regulatory Committee, the import of electricity from the grid in any quarter during the financial year should not exceed 10% of the total generation of electricity by such system, except in case of unforeseen breakdown in the generation system for temporary periods. 91

11 4) A self-sufficient system must not draw power from the utility grid. 5) Constraint based on availability of fuel can be simulated by setting availability of generators to 1 or 0 depending upon whether unit is generating or not. Alternatively, fuel price can be modified if the unit is run with another fuel. 6) Power generation and load balance is expressed by P G = P D. 7) The existing generation can be set as an equality constraint to the objective function Flowchart Figure 4.1 shows the schematic block diagram of the proposed scheme. Initially, the schematic is configured using software; technical and economic details of the system under study are acquired by simulating the system System Study and Controls Wind data : Weibull k : 1.95, 1-hr Auto correlation factor : 0.893, Diurnal Pattern Strength : 0.283, Hours of peak wind speed : 13, Anemometer height : 10 m, Scaled annual averages : 5,6,7,8 m/sec. Diesel Data : Life time 75 kw generator : 30,000 hours, life time 150 kw generator : 40,000 hours, Minimum load ratio : 30%, Lower heating value : 432 MJ/kg, Density : 820 kg/m 3, Carbon Content : 88%, Sulpher content : 0.33% 4.6 CASE : 1 WIND-DIESEL HYBRID POWER SYSTEM After the system components and the equations, Modelling and simulations of the micro power system is carried out. Large number of options are available for different sizes of the components used, components to be added to the system which make sense, cost functions of components used in the system. Optimization and sensitivity analysis algorithms evaluated the possibility of system configuration. Range of different fuel prices and different wind speeds are considered for modeling. The system cost calculations account for costs such as capital, replacement, operation and maintenance, fuel and interest. Figure 4.2 indicates the schematic modeling for Wind- Diesel hybrid system consisting of storage battery, converter and load. 92

12 START Enter the data : Solar Radiation, Wind Speed, Load Details, Cost of different Components j =1 YES Is system in Isolated mode? NO Type of Microgrid Configuration Enter the grid Inputs and Rates and details Calculate the Cost of PV, Wind, NPC and COE Compare the stand alone system with grid connected j =j+1 NO Is NPC(j) minimum? YES Display the techno-economic results of the optimized configuration Figure 4.1 Flowchart of the proposed algorithm for Case : 1 and Case : Isolated Mode The microgrid with one wind generator and two diesel generators along with battery and convertor is simulated and analyzed in isolated mode. Figure 4.2 shows the schematic diagram of Isolated Wind-Diesel Hybrid Power System. The following section discusses all the components used in proposed system in detail. 93

13 Figure 4.2 Schematic Diagram of Isolated Wind-Diesel Hybrid Power System- Case:1 Wind Resource The power generated by a wind turbine can be expressed as: P = 1 2. A. v 3 wind. C p. ρ (4.6) Where A is the area of the rotor blade, v wind is the wind speed, ρ is the air density and Cp is the power coefficient. The power coefficient Cp is a function of the tip speed ratio λ and the blade pitch angle β. Induction generators are very popular in wind turbine applications. They are reliable and well developed. Induction generators are loosely coupled devices i.e. heavily damped and can have ability to absorb slight changes in the rotor speed whilst remaining connected to electric grid. The adoption of Kyoto protocols has some countries looking for the best way to reduce Carbon Dioxide levels and increasing wind generation seems to be the answer. As the ratio of installed wind capacity to the system load increases, the required equipment needed to maintain a stable AC grid increases, forcing an optimum amount of wind power in a given system. So the design of individual components must be sized properly. In this modelling, 65 kw AC rated power is used for the wind turbine. The power curve and cost curve for wind turbine is shown in figures emerged as an important source of sustainable energy resource worldwide. 4.3 and 4.4 respectively. 94

14 Figure 4.3 Power Curve of a Wind Turbine Figure 4.4 Cost Curve of a Wind Turbine The life time is taken as 25 years and Hub Height is 25 meters for the wind turbine considered. Figure 4.5 shows wind resource for the proposed site. Figure 4.5 Wind Resource Diesel Engines Diesel generators and combustion engines are mainly used for off-grid generation. Low installed capacity, high shaft efficiency, suitable for start-stop operation, and high exhaust heat are some of the advantages of combustion engines. These engines convert heat from the combustion into work via rotation of shaft. The shaft is directly coupled to the generator and electricity is produced. They run at a speed defined by the frequency of supply grid. In this modeling 75 kw and 150 kw diesel engines are used along with the wind turbine. Figure 4.6 and 4.7 shows the cost curves of diesel generators rated for 75 kw and 150 kw respectively. Efficiency curve of the diesel generators is shown in figure

15 Figure 4.6 Cost Curve of 75kW Diesel Gen. Figure 4.7 Cost Curve of 150kW Diesel Generator Figure 4.8 Efficiency Curve of Diesel Generator In addition to wind turbine and two diesel generators, storage battery, a converter and a primary load is used in the modeling of Wind-Diesel hybrid system. Load Details The load details for the hybrid system is shown in figure 4.9. The seasonal profile of load is considered with peak load as 207 kw. A primary load of 2.5 MWh/day having load factor is taken for simulation. Figure 4.9 Seasonal Load Details for hybrid Wind-Diesel System Case: 1 96

16 Cost Details Component Wind Diesel Diesel Battery Convertor Turbine Generator1 Generator2 Quantity (kw) Capital ($) Replacement ($) O & M ($/yr) Table 4.2 Cost Details of the Wind-Diesel Hybrid System Components Case : 1 Battery and Convertor details Battery : Nominal Capacity : 1900 Ah, Nominal Voltage : 4V, Round trip efficiency : 80%, Minimum State of Charge : 40%, Maximum charge rate : 1 A/Ah, Maximum charge current : 67.5, Capacity ratio c : 0.251, Rate Constant k: /hr, Maximum capacity : 1882 Ah, Convertor : Lifetime : 25 years, efficiency : 85% Grid Connected Mode The microgrid with one wind generator and two diesel generators along with battery and convertor is simulated and analyzed in isolated mode. Figure 4.10 shows the schematic diagram of grid connected Wind-Diesel Hybrid Power System. The following section discusses all the components used in proposed system in detail. Figure 4.10 Schematic Diagram of Grid Connected Wind-Diesel Hybrid System Case : 1 97

17 Figure 4.11 Optimized Results of Grid Connected Wind-Diesel Systems- Case: Techno-Economic Analysis of Case : 1 Sensitivity Variables A sensitivity variable is an input variable for which multiple values have been specified. HOMER performs a separate optimization procedure for each specified value. HOMER designs an optimal hybrid system for each sensitivity case. A sensitivity analysis can result in a huge amount of output data. Every simulation that HOMER performs results in several dozen summary outputs (like the annual fuel consumption and the total capital cost) plus about a dozen arrays of time series data (e.g. the output of the wind turbine). For the proposed system, wind speed and diesel fuel price are considered to be the sensitivity variables as shown in Table 4.3. Sensitivity Variables Wind Speed (m/s) Diesel Price ($/L) Table 4.3 Sensitivity Variables Case:1 98

18 The simulation is performed for 4*7 = 28 total sensitivity cases and all combinations are considered for simulation. Table 4.4 displays the values of each optimization variable. It shows set of all possible variables in the system configuration. HOMER simulates all possible configurations and sorts them according to net present cost. Total configurations 7*1*1*5*7*4 = 980 simulations are created for the proposed configuration and total possible configurations are 200 which are listed as overall results. Time taken for simulation is 14:46 minutes. Table: 4.4 Search Space Window - Simulation Parameters Case:1 Table: 4.5 Overall Results Window Case : 1 99

19 Table 4.6 Optimized Results Window Case: Results and Discussions Isolated Wind-Diesel Hybrid System The proposed system is simulated and analyzed for three configurations: (i) Diesel only (ii) Diesel Battery and (iii) Wind-Diesel Battery. The results of the simulation are shown in table 4.8 for economic, technical and environmental aspects. PARAMETERS DIESEL DIESEL WIND- ONLY BATTERY DIESEL BATTERY SYSTEM COST NPC($) 3,180,870 3,146,584 2,450,005 COST OF ENERGY ($/Kwh) OPERATING COST ($/yr) 243, , ,557 FUEL CONSUMED (L/yr) WIND TURBINE % DIESEL GENERATOR - 1 7% 20% 28% DIESEL GENERATOR % 80% 21% CO 2 (kg/yr) 743, , ,453 CO (kg/yr) 1,834 1, UNBURNED HYDROCARBON (UH) (kg/yr) PARTICULATE MATTER (PM) (kg/yr)

20 SO 2 (kg/yr) 1,492 1, NO X (kg/yr) 16,365 15,932 8,666 SYSTEM LOAD (kwh/yr) ELECTRICAL EFFICIENCY RENEWABLE FRACTION (RF) 912, , % 33.75% , % Table 4.7 Results of the proposed Wind-Diesel Hybrid System Case:1 The details of the Wind-Diesel hybrid system are shown in the following section. Cost Summary: (i) By Component Figure 4.12 Cost Summary By Component Case:1 (Isolated Mode) Table 4.8 Cost Summary of Different Components By Component Case:1 101

21 (ii) By Cost Type Figure 4.13 Cost Summary By Cost Type Case:1 (Isolated Mode) Figure 4.14 Compare Economic of Diesel Only with Wind-Diesel System (Diesel only Base Case) Figure 4.15 Compare Economics of Diesel Battery with Wind-Diesel System (Diesel Battery Base Case) 102

22 Figures shown above indicate the comparison of economics with Diesel Only and Diesel Battery system with Wind-Diesel System in 4.12 and 4.13 respectively. Table 4.9 shows the comparison of payback period, return on investment, present worth and annual worth. Parameters Diesel Only Diesel Battery Present Worth ($) Annual Worth ($/yr) Return on Investment (%) Internal Rate of Return (%) Simple Payback Discounted Payback Table 4.9 Comparative Parameters of Wind-Diesel Hybrid System Cash Flow Summary: Figure 4.16 Cash Flow Summary Case:1 Electrical Parameters Figure 4.17 Monthly Average Electrical Production Case:1(Isolated Mode) 103

23 Renewable Penetration (%) Hour Figure 4.18 Monthly Renewable Penetration Figure 4.19 Optimal System Type Case: 1 (Isolated Mode) 104

24 Different Cases for Wind-Diesel Hybrid System The proposed hybrid system is tested for the different cases. The cases are shown in the table below. These cases are classified as change in wind speed, change in load, change in fuel price and change in cost of renewable energy technologies. CONDITION SIMULATION CASE A BASE CASE B 10% INCREASE IN WIND SPEED C 10% DECREASE IN WIND SPEED D 10% INCREASE OF LOAD E 10% DECREASE OF LOAD F INCREASE OF DIESEL FUEL PRICE FROM 0.8$/L TO 1.2$/L G 30% COST REDUCTION OF RENEWABLE ENERGY TECHNOLOGIES Table 4.10 Different cases for Wind-Diesel Hybrid System Case:1 For the change in various parameters like change in wind speed, change in load, increase in diesel fuel price and decrease in price of renewable energy technologies, the system is simulated and compared with the base case. The results of the base case Wind-Diesel Hybrid System are shown in the above section. The system is analyzed for Economic parameters like Net Present Cost, Levelized Cost of Energy, Operation and Maintenance Cost and Initial Cost. Also, the technical parameters like share of Wind Turbine and Diesel Generators 1 and 2 along with the Renewable Fraction are simulated and analyzed for the various cases indicated in Table The effect of various Green House Gases emissions like Carbon Dioxide, Carbon Monoxide, Unburned Hydrocarbon, Particulate Matter, Sulphur Dioxide and Nitrogen Oxides are studied and discussed for various cases are shown in Table Effect of Addition of PV into Existing System The proposed Wind-Diesel system is discussed for isolated and grid connected mode for variation of different cases and parameters. In this section of the chapter, the effect of addition of Photovoltaic system, is studied and analyzed. The results and Optimal System Type are shown in table 4.12 and figure 4.20 respectively. 105

25 CONDI A B C D E F G TION IC 52, ,000 52, , , , ,500 O&M 139, , , , , , ,457 COST COE NPC 2,450,005 2,306,587 2,619,440 2,687,307 2,217,797 3,113,091 2,181,206 RF WT 51% 56% 45% 48% 55% 66% 66% DG 1 21% 20% 22% 18% 24% 15% 14% DG 2 28% 24% 33% 34% 21% 19% 20% CO 2 393, , , , , , ,238 CO ,072 1, UH PM SO NO X 8,666 7,907 9,562 9,914 7,454 6,848 7,097 BEGED Table 4.11 Results of Different Cases of Wind-Diesel Hybrid System Parameters Wind-Diesel Hybrid System PV- Wind-Diesel Hybrid System NPC ($) 2,450,005 2,457,058 LCE ($/Kwh) OC ($/yr) 139, ,522 FUEL CONSUMED (L/yr) WIND TURBINE 51% 40% DIESEL GENERATOR-1 28% 22% DIESEL GENERATOR-2 21% 35% 106

26 PHOTOVOLTAIC -- 3% RF CO 2 (kg/yr) 393, ,902 CO (kg/yr) UH (kg/yr) PM (kg/yr) SO 2 (kg/yr) NO X (kg/yr) 8, Table 4.12 Effect of Addition of PV into the Existing System Figure 4.20 Optimal System Type After Addition of PV Grid Connected Wind Diesel Hybrid System The proposed system is simulated and analyzed for two configurations : (i) Wind- Diesel Battery and (ii) Grid Connected Wind-Diesel Hybrid System. The results of the simulation are shown in table 4.13 for economic, technical and environmental aspects. As the system is connected to grid, buy and sale of power from grid is possible as and when required. 107

27 PARAMETERS WIND-DIESEL BATTERY GRID CONNECTED WIND-DIESEL HYBRID SYSTEM SYSTEM COST NPC($) 2,450,005 1,880,330 COST OF ENERGY ($/Kwh) OPERATING COST ($/yr) 139,557 53,525 FUEL CONSUMED (L/yr) WIND TURBINE 51% 76% DIESEL GENERATOR1 28% 8% DIESEL GENERATOR2 21% 1% GRID PURCHASE - 14% CO 2 (kg/yr) 393,453-82,867 CO (kg/yr) UNBURNED HYDROCARBON (UH) (kg/yr) PARTICULATE MATTER (PM) (kg/yr) SO 2 (kg/yr) NO X (kg/yr) 8,666 2,071 AC PRIMARY LOAD (kwh/yr) 912,500 (100%) 912,500 (71%) GRID SALES (kwh/yr) - 376,350 (29%) SYSTEM LOAD (kwh/yr) 912,500 1,288,850 (100%) RENEWABLE FRACTION Table 4.13 Results of the proposed Grid Connected Wind-Diesel Hybrid System The details of the Grid Connected Wind-Diesel hybrid system are shown in the following section: Cost Summary, Cash Flow Summary and Electrical Parameters and Breakeven Grid Extension Distance and Optimal System Type are discussed. 108

28 Cost Summary: (i) By Component Figure 4.21 Cost Summary By Component (Grid Connected Mode Case:1) Table 4.14 Cost Summary of Different Components By Component (Grid Connected Mode Case:1) (ii) By Cost Type Figure 4.22 Cost Summary By Cost Type (Grid Connected Mode Case:1) 109

29 Cash Flow Summary: Figure 4.23 Cash Flow Summary (Grid Connected Mode Case:1) Electrical Parameters Figure 4.24 Monthly Average Electrical Production (Grid Connected Mode Case:1) Figure 4.25 Breakeven Grid Extension Distance (Grid Connected Mode Case:1) 110

30 Figure 4.26 Optimal System Graph Grid Connected Wind-Diesel HPS Discussions Case :1 Wind-Diesel Hybrid Power System : (i) The Wind-Diesel hybrid system is simulated in isolated mode with diesel only, diesel battery and wind-diesel battery configurations. The NPC reduces to 23%, COE reduces to 24% and renewable share in total power generation increases to 51% from Diesel Only configuration. It has been observed a reduction of 50% in GHG emissions, if wind turbine is added into diesel generators. The Wind-Diesel system starts performing better after 5 years if economics of the system is considered. The optimal system type shows the use of diesel generators when the fuel prices are less than 0.45$/L and Diesel battery system is feasible for wind speeds upto 6.5m/s and diesel fuel price ranges from 0.45 to 0.8 $/L. At high diesel prices and high wind speed, penetration of wind shows enhanced system performance. (ii) The proposed system has been checked for various conditions like change in wind speed, change in load, change in fuel price and cost reduction of renewable energy technologies. These cases are compared with the Wind-Diesel Battery hybrid system. Increase in wind speed causes reduction in NPC and COE by 6%, increase in wind penetration by 5% and reduction in GHG emissions by 9%. (iii) The algorithm is tested by adding a PV resource into the existing proposed Wind- Diesel hybrid system. As PV penetration is only 3%, share of diesel generators 111

31 becomes high. Hence, RF, wind penetration reduces and COE, NPC, GHG emissions increases slightly. (iv) An Isolated Wind-Diesel hybrid system is compared with the grid connected hybrid system based on economic, technical and environmental parameters. The results shown in Table 4.13 indicate grid purchase of 14%, due to which the NPC, COE, OC and fuel consumed reduces in the range of 30 to 50%. GHG emissions does not show any powerful impact in case of grid tied system. The power shared by the diesel generators reduces and wind generators increases, hence RF improves for grid connection. 4.7 CASE: 2 PV-WIND-DIESEL HYBRID POWER SYSTEM After the system components and the equations, Modeling and simulations of the micro power system with low load configuration is carried out. Large number of options are available for different sizes of the components used, components to be added to the system which make sense, cost functions of components used in the system. Optimization and sensitivity analysis algorithms evaluated the possibility of system configuration. Range of different fuel prices and different wind speeds are considered for modeling. The system cost calculations account for costs such as capital, replacement, operation and maintenance, fuel and interest. Figure 4.28 indicates the schematic modeling for PV-Wind- Diesel hybrid system consisting of storage battery, converter and load. Wind data : Weibull k : 1.95, 1-hr Auto correlation factor : 0.893, Diurnal Pattern Strength : 0.283, Hours of peak wind speed : 13, Anemometer height : 10 m, Scaled annual averages : 5,6,7,8 m/sec. Solar Data : Average Clearness Index : 0.519, Average Daily Radiation : 5.2 kwh/m 2 /d, Scaled Annual Average : 4.88, 5 kwh/m 2 /d, Lifetime : 20 years, Derating Factor : 90%, Tracking System : Horizontal Axis, Continuous Adjustment Diesel Data : Life time : 15,000 hours, Minimum load ratio : 30%, Lower heating value : 43.2 MJ/kg, Density : 820 kg/m 3, Carbon Content : 88%, Sulpher content : 0.33%. 112

32 4.7.1 Isolated Mode The microgrid with PV-Wind-Diesel Generator along with battery and convertor is simulated and analyzed in isolated mode. Figure 4.28 shows the schematic diagram of Isolated PV-Wind-Diesel Hybrid Power System. The following section discusses all the components used in proposed system in detail. This is AC/DC Hybrid Power System. Figure 4.27 Schematic Diagram of Isolated PV-Wind- Diesel Hybrid System in HOMER Wind Resource In this modelling, 0.4 kw DC rated power is used for the wind turbine. The power curve and cost curve for wind turbine is shown in figures emerged as an important source of sustainable energy resource worldwide 4.29 and 4.30 respectively. Figure 4.28 Power Curve of a Wind Turbine Figure 4.29 Cost Curve of a Wind Turbine 113

33 The life time is taken as 15 years and Hub Height is 25 meters for the wind turbine considered. Figure 4.31 shows wind resource and 4.32 shows wind speed profile for the proposed site. Figure 4.30 Wind Resource Figure 4.31 Wind Speed Profile Solar Resource Solar resource for the site considered is shown in figure Figure 4.32 Solar Resource Diesel Engines In this modeling 1 kw diesel engine is used along with the wind turbine and PV array. Figure 5 shows the cost curve of diesel generator rated for 1 kw. Figure 4.33 Cost Curve of Diesel Gen. Figure 4.34 Efficiency Curve of Diesel Gen. 114

34 In addition to PV, Wind turbine and Diesel Generator, storage battery, a converter and a primary load is used in the modeling of PV-Wind-Diesel hybrid system. Load Details The load details for the hybrid system is shown in figure The seasonal profile of load is considered with peak load as 144 W. A primary load of kwh/day having load factor is taken for simulation. Fig (a) Daily and (ii) Seasonal Load Details for hybrid PV-Wind-Diesel System Cost Details Component PV Wind Diesel Battery Convertor Turbine Generator Quantity (kw) Capital ($) Replacement ($) O & M ($/yr) Table 4.15 Cost Details of the PV-Wind-Diesel Hybrid System Components Battery and Convertor details Battery : Nominal Capacity : 200 Ah, Nominal Voltage : 12V, Round trip efficiency : 80%, Minimum State of Charge : 40%, Maximum charge rate : 1 A/Ah, Maximum charge current : 60, Capacity ratio c : 0.184, Rate Constant k: /hr, Maximum capacity : 193 Ah. Convertor : Lifetime : 15 years, efficiency : 90% 115

35 4.7.2 Grid Connected Mode The microgrid with PV, wind generator and diesel generator along with battery and convertor is simulated and analyzed in isolated mode. Figure 4.36 shows the schematic diagram of Isolated PV-Wind-Diesel Hybrid Power System. The following section discusses all the components used in proposed system in detail. Figure 4.36 Schematic Diagram of Grid Connected PV-Wind- Diesel Hybrid System Figure 4.37 Optimized Results of Grid Connected PV-Wind-Diesel Hybrid Systems The simulation is completed in seconds. The optimized results occur for Solar Radiation 5 kwh/m 2 /d, wind speed 7 m/s and Diesel Price 0.9$/L Techno-Economic Analysis of Case : 1 Sensitivity Variables A sensitivity variable is an input variable for which multiple values have been specified. A sensitivity analysis can result in a huge amount of output data. For the 116

36 proposed system, wind speed and diesel fuel price are considered to be the sensitivity variables as shown in table Sensitivity Solar Radiation Wind Speed Diesel Price Variables (kwh/m 2 /d) (m/s) ($/L) Table 4.16 Sensitivity Variables Case:2 Three sensitivity cases (i) solar annual averages : 4.88 and 5 kwh/m 2 /d, (ii) Wind Speed : 4.5,5,6 and 7 and (iii) Diesel fuel prices : 0.8,0.85 and 0.9 $/L, are considered for simulation. Total 24 sensitivity cases (2*4*3 = 24) with 2880 number of simulations are simulated for the proposed system. Table 4.17 displays the values of each optimization variable. It shows set of all possible variables in the system configuration. HOMER simulates all possible configurations and sorts them according to net present cost. Table : 4.17 Search Space Window - Simulation Parameters PV-Wind-Diesel Case :2 117

37 Table 4.18 Overall Results Window Case : 2 Table 4.19 Optimized Results Window Case : Results and Discussions Isolated PV-Wind-Diesel Hybrid System The proposed system is simulated and analyzed for three configurations: (i) PV- Diesel Battery (ii) PV-Wind Battery and (iii) PV-Wind-Diesel Battery. The results of the simulation are shown in table 4.20 for economic, technical and environmental aspects. The detailed results are shown in the following section. 118

38 PARAMETERS PV-DIESEL PV-WIND PV-WIND- BATTERY BATTERY DIESEL BATTERY SYSTEM COST NPC($) 4,053 3,396 3,159 COST OF ENERGY ($/Kwh) OPERATING COST ($/yr) FUEL CONSUMED (L/yr) PHOTOVOLTAIC 89% 25% 14% WIND TURBINE -- 75% 82% DIESEL GENERATOR % -- 4% CO 2 (kg/yr) CO (kg/yr) UNBURNED HYDROCARBON (UH) (kg/yr) PARTICULATE MATTER (PM) (kg/yr) SO 2 (kg/yr) NO X (kg/yr) SYSTEM LOAD (kwh/yr) ELECTRICAL EFFICIENCY RENEWABLE FRACTION (RF) 25.1% Table 4.20 Results of the proposed PV-Wind-Diesel Hybrid System Figures shown below indicate the Cost summary, comparison of economics with PV- Diesel Battery and PV-Wind-Diesel Battery system. The Present Worth for the compared systems is 894$ and Annual Worth is $ 70/yr. 119

39 Cost Summary: (i) By Component Figure 4.38 Cost Summary By Component Case:2 Table 4.21 Cost Summary of Different Components By Component Case:2 (ii) By Cost Type Figure 4.39 Cost Summary By Cost Type Case:2 120

40 Figure 4.40 Compare Economic of PV-Diesel Battery with PV-Wind-Diesel Battery System (PV-Diesel Battery Base Case) Cash Flow Summary: Figure 4.41 Cash Flow Summary Case:2 Electrical Parameters Figure 4.42 Monthly Average Electrical Production Case:2 121

41 Renewable Penetration (%) Hour Figure 4.43 Monthly Renewable Penetration Case:2 Figure 4.44 Optimal System Type Case:2 (Isolated Mode) Different Cases for PV-Wind-Diesel Hybrid System : Case : 2 The proposed hybrid system is tested for the different cases. The cases are shown in the table below. These cases are classified as change in wind speed, change in load, 122

42 change in fuel price, change in cost of renewable energy technologies and change in solar radiation. CONDITION SIMULATION CASE A BASE CASE B 10% INCREASE IN WIND SPEED C 10% DECREASE IN WIND SPEED D 10% INCREASE OF LOAD E 10% DECREASE OF LOAD F INCREASE OF DIESEL FUEL PRICE FROM 0.8$/L TO 1.2$/L G 1 G 2 H I 30% COST REDUCTION OF RENEWABLE ENERGY TECHNOLOGIES PV 30% COST REDUCTION OF RENEWABLE ENERGY TECHNOLOGIES WIND 5% INCREASE IN SOLAR RADIATION 5% DECREASE IN SOLAR RADIATION Table 4.22 Different cases for PV-Wind-Diesel Hybrid System Case:2 For the change in various parameters as shown in table 4.22, the system is simulated and compared with the base case. PARAMETER IC O & M COST COE NPC A ,159 B 2, ,012 C ,420 D 2, ,350 E ,002 F ,190 G ,851 G ,709 H ,140 I ,144 (a) Economic Parameters for Case : 2 123

43 PARAMETER PV WT DG RF A 14% 82% 4% B 15% 84% 2% C 16% 76% 8% D 15% 81% 4% E 15% 82% 2% F 15% 81% 4% G 1 15% 81% 4% G 2 15% 81% 4% H 16% 81% 3% I 14% 82% 4% (b) Technical Parameters of Case : 2 PARAMETER CO 2 CO UH PM SO 2 NO X A B C D E F G G H I (c) Environmental Parameters of Case : 2 Table 4.23 Results of Different Cases of PV-Wind-Diesel Hybrid System - Case : 2 The results of the base case PV-Wind-Diesel Hybrid System are shown in the above section. The system is analyzed for Economic parameters, technical parameters and environmental parameters are studied and discussed for various are shown in Table 4.23 (a), (b) and (c). 124

44 Effect of Removal of Wind Turbine from the Existing System In this section of the chapter, the effect of removal of Wind Turbine from the existing system, is studied and analyzed. The results are shown in table Parameters PV-Diesel Hybrid System PV- Wind-Diesel Hybrid System NPC ($) 3,977 3,159 LCE ($/Kwh) OC ($/yr) FUEL CONSUMED (L/yr) WIND TURBINE -- 82% DIESEL GENERATOR 22% 4% PHOTOVOLTAIC 78% 14% RF CO 2 (kg/yr) CO (kg/yr) UH (kg/yr) PM (kg/yr) SO 2 (kg/yr) NO X (kg/yr) Table 4.24 Effect of Removal of Wind Turbine from the Existing System Grid Connected PV-Wind Diesel Hybrid System The proposed system is simulated and analyzed for two configurations : (i) Wind- Diesel Battery and (ii) Grid Connected Wind-Diesel Hybrid System. The results of the simulation are shown in table 4.10 for economic, technical and environmental aspects. The details of the Grid Connected PV-Wind-Diesel hybrid system are shown in the following section: Cost Summary, Cash Flow Summary and Electrical Parameters and Breakeven Grid Extension Distance and Optimal System Type are discussed. 125

45 PARAMETERS ISOLATED PV-WIND-DIESEL BATTERY SYSTEM GRID CONNECTED PV- WIND-DIESEL HYBRID SYSTEM SYSTEM COST NPC($) 3,159 2,386 COST OF ENERGY ($/Kwh) OPERATING COST ($/yr) FUEL CONSUMED (L/yr) WIND TURBINE 82% 76% DIESEL GENERATOR 4% 0% PHOTOVOLTAIC 14% 3% GRID PURCHASE - 22% CO 2 (kg/yr) CO (kg/yr) UH (kg/yr) (PM) (kg/yr) SO 2 (kg/yr) NO X (kg/yr) RENEWABLE FRACTION Table 4.25 Results of the proposed Grid Connected PV-Wind-Diesel System Case:2 Cost Summary: (i) By Component Figure 4.45 Cost Summary By Component Case:2 Grid Connected Mode 126

46 Table 4.26 Cost Summary of Different Components By Component Case:2 Grid Connected Mode (ii) By Cost Type Figure 4.46 Cost Summary By Cost Type Case:2 Grid Connected Mode Cash Flow Summary: Figure 4.47 Cash Flow Summary Case:2 Grid Connected Mode 127

47 Electrical Parameters Figure 4.48 Monthly Average Electrical Production Case:2 Grid Connected Mode Figure 4.49 Interpolated Values Case:2 Grid Connected Mode Figure 4.50 Optimal System Graph Grid Connected Wind-Diesel HPS 128

48 Discussions Case :2 PV-Wind-Diesel Hybrid Power System : (i) The PV-Wind-Diesel hybrid system is simulated in isolated mode with pv-diesel battery, pv-wind battery and pv-wind-diesel battery configurations. The NPC reduces to 23%, COE reduces to 24% and renewable share in total power generation due to diesel reduces to 7% from pv-diesel battery system. It has been observed a reduction of around 50% in GHG emissions, if wind turbine is added into pv-diesel battery system. The renewable fraction improves from to in cases of pv-diesel battery and pv-wind-diesel battery system. The optimal system type shows the use of pv-diesel battery system for wind speeds less than 4.65 m/s, pv-wind battery system for wind speed ranging from 4.65 to 5.5 m/s and pv-wind-diesel battery system for wind speeds higher than 5.5 m/s. (ii) The proposed system has been checked for various conditions like change in wind speed, change in load, change in solar radiation, change in fuel price and cost reduction of renewable energy technologies. These cases are compared with the PV- Wind-Diesel Battery hybrid system. Increase in wind speed by 10% causes reduction in NPC and COE by 5%, increase in renewable fraction by 4% and reduction in GHG emissions by 45% compared to the base case. When the load increases by 10%, the NPC increases by 5% and COE reduces by 5%. There exist no significant change in technical and environmental parameters for increase in load. As the pv penetration in the hybrid system is very small, the change in solar radiation cause very nominal change in all parameters. Furthermore, the increase in diesel fuel price, increases the COE, slight reduction in GHG emissions of almost 1%. With the increasing demand, the cost of the renewable technologies reduces. The reduction in cost of renewable technologies by 30%, causes to reduce NPC and COE by 15% in case of wind resource. The reduction of 10% is observed in NPC and COE, if the cost of PV technology reduces by 30%. (iii) The algorithm is tested by removing a wind resource from the existing proposed PV-Wind-Diesel hybrid system. As wind penetration was 82%, share of diesel generators increases. Hence, RF reduces and COE, NPC, GHG emissions, PV penetration increases slightly. 129