Hybrid Optimization Model For Electric Renewable Engineering Essay

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Software. A software package HOMER (Hybrid Optimization Model for Electric Renewable) is used for designing the microgrid system. The software details are specified below.

What is HOMER?

The HOMER (Hybrid Optimization Model for Electric Renewable) software developed by the U.S. National Renewable Energy Laboratory (NREL) to assist in the design of micro-power systems and to facilitate the comparison of power generation technologies across a wide range of applications. HOMER models a power system's physical behaviour and its life-cycle cost which is the total cost of installing and operating the system over its life span. HOMER allows the modeler to compare many different design options based on their technical and economic merits. It also assists in understanding and quantifying the effects of uncertainty or changes in the inputs.

HOMER simplifies the task of evaluating designs of both off-grid and grid-connected power systems for variety of applications. When a user designs power system, the user must make many decisions about the configuration of the system. HOMER performs the analyses to explore a wide range of design questions that are below:

What components does it make sense to include in the system design?

How many and what size of each component should it use?

Which technologies are most cost-effective?

What happens to the project's economics if costs or loads change?

Is the renewable resource sufficient [1]-[26]?

How Does HOMER Work?

HOMER performs three main tasks, which are below:

Simulation

Optimization

Sensitivity analysis

Figure 4.1: Conceptual relationship between simulation, optimization and sensitivity analysis

The above figure 4.1 illustrates the relationship between simulation, optimization and sensitivity analysis. The optimization oval encloses the simulation oval to represent the fact that a single optimization consists of multiple simulations. Similarly, the sensitivity analysis oval encompasses the optimization oval because a single sensitivity analysis consists of multiple optimizations.

Simulation

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 load in the hour to the energy that the system can supply in that hour. 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. HOMER performs energy balance calculations for each system configuration that we want to consider. It then determines whether a configuration is feasible i.e. whether it can meet the electric demand under the conditions that the user specifies 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 (O&M), fuel and interest. A user can then view hourly energy flows for each component as well as annual cost and performance summaries.

Optimization

After simulating all of the possible system configurations, HOMER displays a list of feasible systems, sorted by lifecycle cost. We can easily find the least cost system at the top of the list or we can scan the list for other feasible systems.

Sensitivity Analysis

Sometimes we may find it useful to see how the results vary with changes in inputs, either because they are uncertain or because they represent a range of applications. We can perform a sensitivity analysis on almost any input by assigning more than one value to each input of interest. HOMER repeats the optimization process for each value of the input so that the user can examine the effect of changes in the value on the results. We can specify as many sensitivity variables as we want and analyze the results [1]-[26].

Description of Major Components

In a microgrid power system, a component is any part of a whole power system that generates, delivers, converts or stores energy. The microgrid comprises in four major components that are wind turbine or solar photovoltaics, generator, converter and storage batteries.

There are two intermittent renewable sources for electricity generation that are wind turbines and solar photovoltaics. Wind turbines convert wind energy into ac or dc electricity and PV modules convert solar radiation into dc electricity. Generator is a dispatch-able energy source, meaning that the system can control it as needed and it consumes fuel to produce AC or DC electricity. Converter is used to convert electrical energy into another form and it converts electricity from ac (alternating current) to dc (direct current) or from dc to ac. Finally, storage batteries are used for storing the DC electricity

Wind Turbine

There are some calculations and definitions regarding wind power as follows.

Cut-in Wind Speed

This is the minimum wind speed needed to start the wind turbine (which depends on turbine design) and to generate output power. Usually it is 3 m/s for smaller wind turbines and 5-6 m/s for bigger ones.

Cut-out Wind Speed

The cut-out wind speed represents the speed point where the turbine should stop rotating due to the potential damage that can be done if the wind speed increases more than that [49].

Rated Wind Speed

This is the wind speed at which the wind generator reaches its rated output. Note that not all wind generators are created equal even if they have comparable rated outputs.

Rated Output

This measurement is taken at an uninformed wind speed that the manufacturer designs for. It tends to be at or just below the governing wind speed of the wind generator. Any wind generator may peak at a higher output than the rated output. The faster you spin a wind generator the more it will produce until it overproduces to the point that it burns out. Manufacturers rate their generators at a safe level well below the point of self-destruction.

Peak Output

This figure may be the same as rated output, or it may be higher. Wind generators reach their peak output while governing, which occurs over a range of wind speeds above their rated wind speed [33].

Mean Wind Speed

The mean wind speed for a usual day of a month can be calculated by averaging all the recorded wind speeds for the month.

The mean wind speed is calculated using the equation below [36].

(1)

Where,

Vj observed wind speed (m/s)

Nj number of wind speed observation

Vi mean wind speed (m/s)

Upgrading the Mean Wind Speed

The mean wind speeds are then upgraded to the hub height. Wind speeds increase with height [37]. The calculated mean wind speeds are speeds recorded near the ground surface. Since the hubs of wind turbine are usually more than ten meters high, the mean wind speeds at a particular height will be greater than Vi. Therefore, to obtain mean wind speeds, Vi has to be projected to the hub height. The projected Vi is calculated using the power-law equation shown [38]-[39].

(2)

Where,

VZ : mean wind speed at projected height Z

Vj : mean wind speed at reference height Zj (usually 10m)

Z : projected height, or hub height

Zj : reference height, usually 10m

X : power-law exponent

The power-law exponent, x depends upon the roughness of the surface. For open land, x is usually taken as 1/7.

Weibull Distribution

A random variable v can be expressed with a Weibull distribution by utilizing the probability density function (pdf) as given by Stevens and Smulders [34] and shown below:

(3)

Where c is a scale parameter with the same units as the random variable and k is a shape parameter.

Power Output

The electric power output of a wind turbine is primarily a function of wind speed [35] and as shown below:

(4)

Where,

Vi : the cut-in wind speed

Vr : the rated wind speed

Vo : the cut-out wind speed

Pr : the rated electrical power

As illustrated in Figure below:

Figure 4.2: Wind Turbine generator power curve

The average wind power output from a wind turbine is the power produced at each wind speed multiplied by the fraction of the time that wind speed is experienced and integrated over all possible wind speeds. The average power output of a turbine is a very important parameter for any wind power system since it determines the total energy production and hence the total income. It is a much better indicator of economics than the rated power, which can easily be chosen at too large a value.

The equation in integral form is as follows:

(5)

The formula of average wind power output can be obtained by substituting (3) and (4) into (5), which gives equation (6) below [9]:

(6)

The software HOMER models a wind turbine as a device that converts the kinetic energy of the wind into AC or DC electricity according to a particular power curve, which is a graph of power output versus wind speed at hub height. An example of power curve is shown in figure 4.3 below:

Figure 4.3: Wind turbine power curve

HOMER assumes that the power curve applies at a standard air density of 1.225 kg/m3 which corresponds to standard temperature and pressure conditions. Each hour, HOMER calculates the power output of the wind turbine in a four-step process. First, it determines the average wind speed for the hour at the anemometer height by referring to the wind resource data. Second, it calculates the corresponding wind speed at the turbine's hub height using either the logarithmic law or the power law. Third, it refers to the turbine's power curve to calculate its power output at that wind speed assuming standard air density. Fourth, it multiplies that power output value by the air density ratio, which is the ratio of the actual air density to the standard air density. Note that, HOMER calculates the air density ratio at the site elevation using the U.S. Standard Atmosphere [40] and assumes that the air density ratio is constant throughout the year.

In addition to the turbine's power curve and hub height, the design engineer specifies the expected lifetime of the turbine in years, its initial capital cost in U.S. dollars ($), its replacement cost in dollars and its annual O&M (operation & maintenance) cost in dollars per year [26].

PV Array

Power output of the PV array can be calculated using the equation below:

Where,

= PV derating factor

= Rated capacity of PV array (kW)

= Global solar radiation incident on the surface of PV array (kW/m2)

= 1 kW/m2 (The standard amount of radiation used to rate the capacity of PV array)

The rated capacity sometimes called the peak capacity of a PV array is the amount of power it would produce under standard test conditions of 1 kW/m2 irradiance and a panel temperature of 25oC.

The engineering software package HOMER is used for modelling the hybrid power system, in the software the size of PV array is always specified in terms of rated capacity. The rated capacity accounts for both the area and the efficiency of PV module, so neither of those parameters appears clearly in the software. The software itself calculate each hour of the year global solar radiation incident on the PV array using the Hay, Davis, Klucher, Reindl (HDKR) model of Duffie and Beckmann [28]. The derating factor is a scaling factor meant to account for effects of dust on the panel, wire losses, elevated temperature or anything else that would cause the output of the PV array to deviate from that expected under ideal conditions. The HOMER software does not account for the fact that the power output of a PV array decreases with increasing panel temperature but we can reduce the derating factor to (crudely) correct for this effect when modelling systems for hot climates.

In reality the output of a PV array does depend strongly and nonlinearly on the voltage to which it is exposed. The maximum power point (the voltage at which the power output is maximized) depends on the solar radiation and the temperature. If the PV array is connected directly to a dc load or a battery bank then it will often be exposed to a voltage different from the maximum power point and performance will suffer. A maximum power point tracker (MPPT) is a solid state device placed between the PV array and the rest of the dc components of the system that decouples the array voltage from that of the rest of the system and ensures that the array voltage is always equal to the maximum power point. By ignoring the effect of voltage to which the PV array is exposed, HOMER effectively assumes that a maximum power point tracker is present in the system.

To explain the cost of PV array the user specifies its initial capital cost in U.S. dollars ($), replacement cost in dollars and O&M (operating and maintenance) cost in dollars per year. The replacement cost is the cost of replacing the PV array at the end of its useful lifetime which the user specifies in years. By default the replacement cost is equal to the capital cost but the two can differ for several reasons [1]-[26]-[27]

Generator

A generator consumes fuel to produce AC or DC electricity. The generator can be AC or DC and can consume a different fuel. The principal physical properties of the generator are its maximum and minimum electrical power output, its expected lifetime in operating hours, the type of fuel it consumes and its fuel curve which relates the quantity of fuel consumed to the electrical power produced. A generator can consume any of the fuels listed in the fuel library in the software package HOMER. A diesel generator is used for the microgrid system. The software assumes the fuel curve is a straight line with a y-intercept and uses the following equation for the generator's fuel consumption:

F = F0Ygen + F1Pgen

Where F0 is the fuel curve intercept coefficient, F1 is the fuel curve slope, Ygen the rated capacity of the generator (kW) and Pgen the electrical output of the generator (kW). The units of F depend on the measurement units of the fuel. If the fuel is denominated in litres then the units of F are L/h. If the fuel is denominated in m3 or kg then the units of F are m3/h or kg/h respectively. In the same way the units of F0 and F1 depend on the measurement units of the fuel. For fuels denominated in litres the units of F0 and F1 are L/h.kW.

For a generator that provides heat as well as electricity, the design engineer also specifies the heat recovery ratio. HOMER assumes that the generator converts all the fuel energy into either electricity or waste heat. The heat recovery ratio is the fraction of that waste heat that can be captured to serve the thermal load. In addition to these properties, the modeller can specify the generator emissions coefficients, which specify the generator's emissions of six different pollutants in grams of pollutant emitted per quantity of fuel consumed.

The design engineer can schedule the operation of the generator to force it ON or OFF at certain times. During times that the generator is neither forced ON or OFF, HOMER decides whether it should operate based on the needs of the system and the relative costs of the other power sources. During times that the generator is forced ON, HOMER decides at what power output level it operates which may be anywhere between its minimum and maximum power output.

The design engineer specifies the generator's initial capital cost in U.S. dollars ($), replacement cost in dollars and annual O&M (operation & maintenance) cost in dollars per operating hour also. The generator O&M cost should account for oil changes and other maintenance costs, but not fuel cost because HOMER calculates fuel cost separately. As it does for all dispatch-able power sources, HOMER calculates the generator's fixed and marginal cost of energy and uses that information when simulating the operation of the system. The fixed cost of energy is the cost per hour of simply running the generator without producing any electricity. The marginal cost of energy is the additional cost per kilowatt-hour of producing electricity from that generator.

HOMER uses the following equation to calculate the generator's fixed cost of energy:

Cgen,fixed = Com,gen + Crep,gen/Rgen + F0YgenCfuel,eff

Where Com,gen is the O&M cost in dollars per hour, Crep,gen the replacement cost in dollars, Rgen the generator lifetime in hours, F0 the fuel curve intercept coefficient in quantity of fuel per hour per kilowatt, Ygen the capacity of the generator (kW) and Cfuel,eff the effective price of fuel in dollars per quantity of fuel. The effective price of fuel includes the cost penalties if any associated with the emissions of pollutants from the generator.

HOMER calculates the marginal cost of energy of the generator using the following equation:

Cgen,mar = F1Cfuel,eff

Where F1 is the fuel curve slope in quantity of fuel per hour per kilowatt-hour and Cfuel,eff is the effective price of fuel (including the cost of any penalties on emissions) in dollars per quantity of fuel [26].

Battery Bank

Although renewable resources are attractive, they are not always dependable in the absence of energy storage devices. As a result, renewable resources are often used together with energy storage devices. However, in many cases, such systems are the least understood and the most vulnerable component of the system [56]. Among different types of energy storage devices, lead-acid batteries are still the most commonly used devices to store and deliver electricity in the range from 5V to 24V DC [57]-[58].

The battery bank is a collection of one or more individual batteries. The software package HOMER models a single battery as a device capable of storing a certain amount of dc electricity at a fixed round-trip energy efficiency with limits as; how quickly it can be charged or discharged, how deeply it can be discharged without causing damage and how much energy can cycle through it before it needs replacement. HOMER assumes that the properties of the batteries remain constant throughout its lifetime and are not affected by external factors such as temperature.

In HOMER, the key physical properties of the battery are its nominal voltage, capacity curve, lifetime curve, minimum state of charge and round-trip efficiency. The capacity curve shows the discharge capacity of the battery in ampere-hours versus the discharge current in amperes. Manufacturers determine each point on this curve by measuring the ampere-hours that can be discharged at a constant current out of a fully charged battery. Capacity typically decreases with increasing discharge current. The lifetime curve shows the number of discharge-charge cycles the battery can withstand versus the cycle depth. The number of cycles to failure typically decreases with increasing cycle depth. The minimum state of charge is the state of charge below which the battery must not be discharged to avoid permanent damage. In the system simulation, HOMER does not allow the battery to be discharged any deeper than this limit. The round-trip efficiency indicates the percentage of the energy going into the battery that can be drawn back out.

Figure 4.4: Kinetic battery model concept

To calculate the battery's maximum allowable rate of charge or discharge, HOMER uses the kinetic battery model [41] which treats the battery as a two tank system as illustrated in the figure above. According to the kinetic battery model part of the battery's energy storage capacity is immediately available for charging or discharging but the rest is chemically bound. The rate of conversion between available energy and bound energy depends on the difference in 'height' between the two tanks. Three parameters describe the battery. The maximum capacity of the battery is the combined size of the available and bound tanks. The capacity ratio is the ratio of the size of the available tank to the combined size of the two tanks. The rate constant is analogous to the size of the pipe between the tanks. . The kinetic battery model explains the shape of the typical battery capacity curve as shown in figure 4.5 below:

Figure 4.5: Capacity curve for deep-cycle battery model US-250 [42]

Modelling the battery as a two-tank system rather than a single-tank system has two effects. First, it means the battery cannot be fully charged or discharged all at once, a complete charge requires an infinite amount of time at a charge current that asymptotically approaches zero. Second, it means that the battery's ability to charge and discharge depends not only on its current state of charge but also on its recent charge and discharge history. A battery rapidly charged to 80% state of charge will be capable of a higher discharge rate than the same battery rapidly discharged to 80%, since it will have a higher level in its available tank. HOMER tracks the levels in the two tanks each hour and models both these effects.

Figure 4.6: Lifetime curve for deep-cycle battery model US-250

The above figure shows a lifetime curve of a deep-cycle lead-acid battery. The number of cycles to failure (shown in the graph as the lighter-coloured points) drops sharply with increasing depth of discharge. For each point on this curve, one can calculate the lifetime throughput (the amount of energy that cycled through the battery before failure) by finding the product of the number of cycles, the depth of discharge, the nominal voltage of the battery and the aforementioned maximum capacity of the battery. The lifetime throughput curve as shown in the above figure as black dots typically shows a much weaker dependence on the cycle depth. HOMER makes the simplifying assumption that the lifetime throughput is independent of the depth of discharge. The value that HOMER suggests for this lifetime throughput is the average of the points from the lifetime curve above the minimum state of charge but the user can modify this value to be more or less conservative.

The assumption that lifetime throughput is independent of cycle depth means that HOMER can estimate the life of the battery bank simply by monitoring the amount of energy cycling through it, without having to consider the depth of the various charge-discharge cycles. HOMER calculates the life of the battery bank in years as:

Rbatt = min (NbattQlifetime / Qthrpt , Rbatt,f )

Where,

Nbatt number of batteries in the battery bank

Qlifetime lifetime throughput of a single battery

Qthrpt annual throughput (the total amount of energy that cycles through the battery bank in one year).

Rbatt,f float life of the battery (the maximum life regardless of throughput).

The user specifies the battery bank's capital and replacement costs in U.S. dollars ($) and the O&M (operating & maintenance) cost in dollars per year. Since the battery bank is a dispatch-able power source, HOMER calculates its fixed and marginal cost of energy for comparison with other dispatch-able sources. Unlike the generator, there is no cost associated with operating the battery bank so that it is ready to produce energy; hence its fixed cost of energy is zero. For its marginal cost of energy, HOMER uses the sum of the battery wear cost (the cost per kilowatt-hour of cycling energy through the battery bank) and the battery energy cost (the average cost of the energy stored in the battery bank). HOMER calculates the battery wear cost as below:

Cbw = (Crep,batt / NbattQlifetime rt)

Where,

Crep,batt replacement cost of the battery bank

Nbatt number of batteries in the battery bank

Qlifetime lifetime throughput of a single battery (kWh)

rt round-trip efficiency

HOMER calculates the battery energy cost each hour of the simulation by dividing the total year-to-date cost of charging the battery bank by the total year-to-date amount of energy put into the battery bank. Under the load-following dispatch strategy, the battery bank is only ever charged by surplus electricity, so the cost associated with charging the battery bank is always zero. Under the cycle-charging strategy however, a generator will produce extra electricity (and hence consume additional fuel) for the express purpose of charging the battery bank, so the cost associated with charging the battery bank is not zero [26].

Converter

A converter is a device that converts electric power from DC to AC in a process called inversion and/or from AC to DC in a process called rectification. The software HOMER can model the two common types of converters that are solid-state and rotary. The converter size which is a decision variable refers to the inverter capacity, meaning the maximum amount of AC power that the device can produce by inverting DC power. The model design engineer specifies the rectifier capacity which is the maximum amount of DC power that the device can produce by rectifying AC power as a percentage of the inverter capacity. The rectifier capacity is therefore not a separate decision variable. HOMER assumes that the inverter and rectifier capacities are not surge capacities that the device can withstand for only short periods of time but rather continuous capacities that the device can withstand for as long as necessary.

The HOMER user indicates whether the inverter can operate in parallel with another AC power source such as a generator or the grid. Doing so requires the inverter to synchronize to the AC frequency, an ability that some inverters do not have. The final physical properties of the converter are its inversion and rectification efficiencies which HOMER assumes to be constant. The economic properties of the converter are its capital and replacement cost in U.S. dollars ($), its annual O&M (operation & maintenance) cost in dollars per year and its expected lifetime in years [26].

Domestic Load

Load Profile

In electrical engineering, a load profile is a graph of the variation in the electrical load versus time. A load profile will vary according to customer type (typical examples include residential, commercial and industrial), temperature and holiday seasons. In the electricity generation sector, a load curve is a chart showing the amount of electricity customer's use over a period of time. Generation companies use this information to plan how much power they will need to generate at any given time [61].

Load Profile is a broad term that can refer to a number of different forms of data. It can refer to demand and consumption data or it can be a reference to derived data types, such as Regression and Profile Coefficients. However, all these data types have one thing in common that they represent the pattern of electricity usage of a segment of supply market customers [60].

Load Factor

Load factor is the average power divided by the peak power, over a period of time. The peak may be a theoretical maximum, rather than a measured maximum [62]-[64].

A Peak Load Factor is defined as follows:

The ratio expressed as a percentage of the number of kWh supplied during a given period to the number of kWh that would have been supplied had the maximum demand been maintained throughout that period [60].

So for an Annual Peak Load Factor:

LF = [(Annual Consumption (kWh)) / (Maximum Demand (kW) * Number of Hours in the Year)] * 100

Note: 8760 hours or 8784 hours in a leap year

Domestic Load Profile Scenarios

There are two different types of domestic load profiles case studies scenarios for the simulations of microgrid system, which are as follows:

Domestic Load Profile in UK

Domestic Load Profile in Sultanate of Oman

Domestic Load Profile in UK

The load profile mostly depends on occupancy pattern so analyzing the load profile, it is necessary to identify the cluster of household. I worked on five most common cases or scenarios of UK domestic occupancy pattern due to having less information regarding household occupancy pattern, which are under below:

Scenario 1:

In this case unoccupied period is from 09:00 to 13:00. One of the occupants may have part time job in the morning in this type of household occupancy pattern.

Scenario 2:

Unoccupied period is from 09:00 to 16:00. The occupants in the house all have full time job.

Scenario 3:

Here unoccupied period is from 09:00 to 16:00. This type of household occupants may have a child to look after when school closed.

Scenario 4:

In this case the house is occupied all the time because this type of household occupants may have retired couples, children to look after and single.

Scenario 5:

Unoccupied period is from 13.00 to 18.00. One of the occupants in this type of household may have a part time job in the afternoon session [59].

UK Domestic Typical Profile (Averge of All):

This is the average of above all five different scenarios of domestic load profile pattern in the UK at the present.

Domestic Load Profile in Sultanate of Oman

In Oman, the load profile depends on user electricity consumption and occupancy; so analyzing the load profile, it is necessary to identify the group of household. I worked on three most common scenarios of Oman household occupancy pattern depending on low consumption, medium consumption and high consumption electricity users. All three types of scenarios are calculated on assumption based with the average of all four seasons (spring, summer, autumn and winter), that are below:

Scenario 1:

There are two people living in one bed room house in this type of occupancy and are low electricity consumption users. The average of all four season's graph is shown below.

Scenario 2:

This type of occupancy is under four persons living in three bed room house and they are medium electricity users.

Scenario 3:

In this scenario, there are five persons living in five bed room house. One of the occupants may have full time job, one school child and they are high electricity users.

Pakistan Domestic Typical Profile (Average of All):

This is the typical load profile which is the average of above three different scenarios of domestic load profile in Oman.

5.1 Introduction

There are two different case studies carried out in the two different places for the design of microgrid power system. The first one is wind/diesel/battery hybrid power system within the microgrid in the UK, and the other is photovoltaic/diesel/battery hybrid power system in the Oman. The both case studies are specified below:

Design Case Study 1

Design Case Study 2

Design Case Study 1

Introduction

The first case study for designing the microgrid system is carried out in the Isle of Arran, Scotland, UK. Arran is the seventh largest island in the Scotland and it is in the North Ayrshire unitary council area. Arran is located within the latitude and longitude of 55o 34'N, 05o 12'W. It has an area of 167 square miles (432 square kilometres), 874 meters height and 50 miles from Glasgow City. Temperatures are generally cool, averaging about 6 °C (43 °F) in January and 14 °C (57 °F) in July at sea level. The southern half of the island, being less mountainous has a more favourable climate than the northern half and the east coast is more sheltered from the prevailing winds than the west and south.

Figure 5.1: Isle of Arran [Source: Google Map®]

Wind energy is one the best renewable energy resource (RES) in Scotland and Isle of Arran is the best location due to fast winds blowing. Also RES's are the most cost effective, reliable and environment friendly sources of electricity generation for the island areas [65]-[66]-[68].

It was decided to use available renewable energy sources based on hourly or daily energy consumption by implementing a small scale microgrid power system. This hybrid power system (wind/diesel/battery) combines into wind turbine, diesel generator, storage batteries, converter and some power electronic equipments. The HOMER (Hybrid Optimization Model for Electric Renewable) software is used for the modeling and simulations of the microgrid system.

System Design

To verify the reduction in carbon emissions or green house gases (GHG) and economic viability, a microgrid hybrid power system is proposed to feed a typical house located in remote area of the Isle of Arran, Scotland. The model consists of wind turbine, diesel generator, battery bank, converter, domestic load and AC & DC busbars. The HOMER software is used for modeling the system design, simulation, economic analysis and calculation of green house gases (GHG). Monthly average wind resources data and average domestic loads are used as input parameters. The schematic diagram of the microgrid power system is modeled in the HOMER, which is shown in figure 5.2 below:

Figure 5.2: Schematic diagram of microgrid system

The average load profile of the UK domestic household or remote household is below in figure 5.3 and load parameters can be seen in the table 5.1 below:

Figure 5.3: Load profile

Table 5.1: Load Parameters

Baseline

Scaled

Average (kWh/day)

10.6

10.6

Average (kW)

0.443

0.443

Peak (kW)

1.10

1.10

Load Factor

0.402

0.402

Monthly average wind speed data of specific location 'Isle of Arran' is collected from weather underground [70]; figure 5.4 shows the average wind speed data as below:

Figure 5.4: Average wind speed

The table 5.2 shows the calculated Weibull distribution parameters (shape parameter and scale parameter) and figure 5.5 shows the Weibull distribution of wind speed as follows:

Table 5.2: Weibull distribution parameters

Shape parameter, k

Scale parameter, c

2.00

3.67

Figure 5.5: Weibull distribution of wind speed

The Skystream 3.7 1.8 kW turbine is used for wind power generation and it is suitable for powering rural domestic properties and many more applications. This wind turbine is intended for a range of conditions especially rural locations. The design life of the machine is 20 years and has been extensively tested by the US Government's NREL organization [71]. Figure 5.6 shows the proposed 1.8 kW wind turbine with tower and figure 5.7 shows the particular wind turbine power curve.

Figure 5.6: Skystream 3.7 1.8 kW wind turbine [71]

Figure 5.7: Wind turbine power curve

Wind turbine specifications are as follows:

Rated power: 1.8 kW

Rotor diameter: 3.7 m

Hub height: 25 m

Cut-in speed: 3.5 m/s

Cut-out speed: 27-33 m/s

Tip speed: 9.7-63 m/s

Survival wind speed: 63 m/s

No. of blades: 3

Rotor orientation: Downwind

Blade material: Fiberglass reinforced composite

Table 5.3 below shows the cost of microgrid components. In the generator, diesel is used as a fuel and its annual average price 0.753 $/litre [75] is assumed. Generator has 15,000 operating hours (lifetime), wind turbine has 20 years lifetime, converter has 15 years lifetime with 90% efficiency, 1 battery bank comprises in 8 batteries, each of nominal 6V & nominal capacity of 360 Ah.

Table 5.3: Cost of microgrid components

System Components

Capital Cost

in $

Replacement Cost

in $

O&M Cost

in $

Wind Turbine

1.8 kW

14000

7000

300 $/yr

Generator

1 kW

500

400

0.05 $/hr

Converter

1 kW

500

500

100 $/yr

Battery

(8 nos.)

300/Battery

300/Battery

$/yr

Simulation Results

The figure 5.8 below shows the wind/diesel/battery hybrid power system's simulation result summary. This is the main graph of system's result which shows the monthly average electricity production and renewable fraction. It shows the contributions of wind turbine generation and generation by diesel generator for the microgrid system. The wind turbine contribution (renewable fraction) towards electricity generation is 27% (1177 kWh/year) and rest of the generation is 73% (3156 kWh/year) which is produced by generator, as can be seen in figure 5.9 and figure 5.10 below:

Figure 5.08: Monthly average electricity production

Figure 5.09: Simulation results

Figure 5.10: Electricity production summary

The figure 5.11 shows the results of reduction of green house gases (GHG) emissions by using wind/diesel/battery hybrid system.

Figure 5.11: Results of GHG emissions

The figure 5.12 and 5.13 below shows the cash flow summary of the each microgrid component and total cost of the complete system in U.S. Dollars ($). It shows 41,897$, the total cost of the system.

Figure 5.12: Cash flow summary

Figure 5.13: Cash summary of the complete system

Note:

In a politically supported drive towards a low-carbon economy, the UK government now provides grants of up to £2500 per property towards the cost of installing low carbon micro generation technologies such as micro-wind turbines, solar electricity, solar water heating systems, small-scale hydro power systems, ground source heat pumps and biomass boilers etc [8]-[73].

So, as a result the total cost of the microgrid system will reduce. So, new total cost of the system would be:

New Total Cost ($) = 41,897 - 3,788

= 38,109 $ or £ 25,235

The new total cost of installing wind/diesel/battery microgrid system in 'Isle of Arran', Scotland is 38,109 $ or £ 25,235 [72].

Design Case Study 2

Introduction

The second case study for microgrid system design is carried out in the Sultanate of Oman. [67]-[68].

Figure 5.14: Multan [Source: Google Map®]

Solar energy is one of the most promising renewable energy sources in Pakistan and Multan is in the best locations for harnessing the solar power in the Pakistan. So that is why this location is selected for photovoltaic (PV) generation. Solar energy is more predictable than wind energy and less vulnerable to changes in seasonal weather patterns than hydropower. Solar energy can produce power at the point of demand in both rural and urban areas [69].

The hybrid power system (photovoltaic/diesel/battery) combines into PV modules, diesel generator, storage batteries, converter and some power electronic equipments. The HOMER software is used for design and simulations of the microgrid system.

System Design

Reducing the green house gases (GHG) emissions and economic viability, a microgrid hybrid power system is planned to provide electricity for a typical house located in remote area in Multan, Pakistan. The model consists of photovoltaic arrays, diesel generator, battery bank, converter, domestic load and AC & DC busbars. The HOMER software is used for modeling the system design, simulation, economic analysis and calculation of green house gases (GHG). Monthly average solar radiations and average domestic loads are used as input parameters. The figure 5.15 below shows the schematic diagram of the microgrid system.

Figure 5.15: Schematic diagram of microgrid system

The average load profile of the Pakistan domestic household is below in the figure 5.16 and load parameters can be seen in the table 5.4 below:

Figure 5.16: Load profile

Table 5.4: Load Parameters

Baseline

Scaled

Average (kWh/day)

24.3

24.3

Average (kW)

1.01

1.01

Peak (kW)

1.70

1.70

Load Factor

0.597

0.597

The average monthly solar radiations data of particular location 'Multan' is collected from the Science Direct research paper "Prospects for secure and sustainable electricity supply for Pakistan" [69]. The figure 5.17 below shows the average solar radiations data.

Figure 5.17: Average solar radiation

The figure 5.18 below shows the solar panels mounted on the house roof.

Figure 5.18: Solar panels mounted on the roof

The table 5.5 shows the cost and approximate area required for mounting the solar panels on the house roof.

Table 5.5: PV module and approximate area

PV-Module (Watts)

Approximate Roof Area

Required (m2)

Approximate Cost

in $/Watt

2000

18.58

4 to 5

Table 5.6 below shows the cost of microgrid components. In the generator, diesel is used as a fuel and its annual average price 0.753 $/litre [75] is assumed. Generator has 15,000 operating hours (lifetime), PV array capital cost is 8000$ which I got it from "free sun power" [76] and it has 20 years lifetime, converter has 15 years lifetime with 90% efficiency, 1 battery bank comprises in 8 batteries, each of nominal 6V & nominal capacity of 360 Ah.

Table 5.6: Cost of microgrid components

System Components

Capital Cost

in $

Replacement Cost

in $

O&M Cost

in $

PV Module

2 kW

8000

6000

20 $/yr

Generator

1 kW

500

400

0.05 $/hr

Converter

1 kW

500

500

100 $/yr

Battery

(8 nos.)

300/Battery

300/Battery

$/yr

Simulation Results

The figure 5.19 below shows the photovoltaic/diesel/battery system simulation result summary. It shows the contributions of photovoltaic generation and generation by diesel generator for the microgrid system. The photovoltaic's contribution (renewable fraction) towards electricity generation is 42% (4371 kWh/year) which is quite good and rest of the generation is 58% (5991 kWh/year) which is produced by generator, as can also be seen in figure 5.20 and figure 5.21 below:

Figure 5.19: Monthly average electricity production

Figure 5.20: Simulation results

Figure 5.21: Electricity production summary

The figure 5.22 shows the results of reduction of green house gases (GHG) emissions by using photovoltaic/diesel/battery hybrid system.

Figure 5.22: Results of GHG emissions

The figure 5.23 and 5.24 below shows the cash flow summary of the each microgrid component and total cost of the complete system in U.S. Dollars ($). It shows 44,069$, the total cost of the system.

Figure 5.23: Cash flow summary

Figure 5.24: Cash summary of the complete system

So, as a result the total cost of the photovoltaic/diesel/battery microgrid system for Multan, Pakistan is 44,069$ or £29,080 [72].

6.1 Discussions

From the study of this project it can be summarizing that the simulation results of energy production by small scale generators (conventional and non-conventional) in close proximity to the energy users, integrated into microgrid, can manage to feed the load efficiently with quality clean or green power. The system generates power with the reduction of harmful green-house gases (GHG) emissions as compared to pure conventional power system, which makes global warming. The system is efficient enough to meet the domestic load requirements, and the system can be more efficient and eco-friendly if the wind turbine and solar photovoltaics generate more electricity to make the system more greener or environmental friendly.

6.2 Problems Occurred

First of all, at the beginning of the project I had difficulties to find proper software for modeling and simulating the microgrid system. I found various types software's for designing hybrid system, but they were not actually suitable for my proposed system design. After searching a lot I found HOMER software for designing, modeling and simulating the microgrid system with the help of Prof. Chengke Zhou.

Finding wind resource data was another issue to concern because the main weather forecast department 'Met-office' charges for giving annual wind resource or solar radiations data. But at last I found the required annual wind resources data from weather underground website.

Designing the microgrid system on HOMER was the main issue because I never used this software before or neither designed any hybrid system. So, it was a challenge for me to deal with. Because making reliable, economical and efficient microgrid system; the right specification of the each component had to be considered. But working on HOMER software for designing microgrid system has been very useful with great experience.

Designing two different microgrid systems for two different remote locations was a big challenging task because a lot research and design work was involved and it was lengthy as well due to two different case studies.

6.3 Possible Achievements

I have achieved and learned a lot of conceptual and broad knowledge related to this project like software skills, system design calculations, impotency of renewable energies and designing small scale electric power system for electrifying remote locations or individual consumers.

6.4 Conclusions

The study highlighted the increasing requirement for the combination of renewable energy systems at the distributed generation level. Small scale wind turbines and PV modules have found applications in numerous sectors including domestic.

From overall project, designed microgrid power systems for both case studies contribute their part to reduce greenhouse gasses emissions (GHG) which makes the world warming; also designed systems meet the requirements of remote load efficiently. Case study 1 (wind/diesel/batter hybrid system) in the Isle of Arran, Scotland, UK has 27% renewable fraction which is quite good, the case study 2 (photovoltaic/diesel/battery hybrid system) in Multan, Punjab, Pakistan has 42% renewable fraction which is very good result and the overall system is also economical because Pakistan has a lot potential in solar energy.

As a result, the total cost of case study 2 is higher than the case study 1 because it has more domestic load rather than case study 1 and had to design system with 2 kW PV module, also there is not governmental support included in the system because government of Pakistan does not contribute towards installing small scale renewable energy system, but overall case study 2 contributes more towards reduction of carbon emissions.

Overall, the principal conclusion is that microgrid systems do have real potential to make a major contribution to reducing GHG emissions from individual or domestic locations. This will only happen if there are major changes to the electricity market and regulatory structure.

6.5 Future Outwork

After done this project there are some recommendations for further work in order to improve or efficient the power system:

If the designed wind turbine cut in speed is less or minimum rather than 3.5 m/s (the used Skystream wind turbine) then the wind turbine would produce more power, or if we install the system in a particular area where the wind speeds are really high then the system would generate more power.

If the PV module quality can be improved then we can get more power by photovoltaic system, so have to work on PV module material.

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