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In this paper, a stand-alone hybrid microgrid consisting of wind turbines, photovoltaic (PV) arrays and storage battery banks is developed for use in Qinghai Province, China. With the help of Software Homer and Matlab, different variables such as annual average wind speed, annual average load demand, and annual capacity shortage are considered. The net present value is then used during an entire project lifetime for the optimization solution.

It is necessary to develop some renewable energy resources in some remote rural areas where the grid extension is unavailable technologically or economically. Some renewable energy resources are used to support local facilities. For instance, the International Resources Group conducted a survey in Sri Lanka where people use a 50-W PV/battery system to support several compact fluorescent lights [

This paper models a stand-alone hybrid system in Qinghai Province, China, where several households are regarded as an object group. Three variables i.e. average annual wind speed, average daily load demand and annual capacity shortage are set with different values for more valid comparison.

In this case, the hourly wind speeds in a period are recorded. After wind resource data is produced, the wind speed at hub height can be matched. Either the logarithmic law or the power law can be used to achieve this. The power law formula is used here [

v-wind speed at hub height, H (m/s)

v_{i}-wind speed at a reference height, H_{i} (m/s)

There are 8760 hours in one year and as the simulation is done hourly which means it is the number of simulation steps. Each wind speed matches an output of power according to the wind turbine’s power curve under standard conditions of temperature and pressure. Different wind turbines have different power curves provided by manufacturers. In this case, the SW AIR-X wind turbine is used and its power curve is shown in

Other factors that can affect the overall efficiency of wind turbine output such as environmental losses, wake effects losses, curtailment losses are not considered. In this paper, these are not included in the design process.

For solar sources, the solar radiation incident on the PV array and the ambient temperature at different hours are also recorded. Combining them together and assuming the use of maximum power point tracking (MPPT) which is a technique used with wind turbines and PV solar systems to maximize power extraction under all conditions, the output of a particular PV array can be calculated as in [

where P_{pv-r} is the rated capacity of the PV array under its standard test conditions, f_{pv} is derating factor. G_{stc} and T_{stc} are the incident radiation on the PV array and the PV cell temperature under normal test conditions, respectively. G

and T are the incident radiation and temperature of the PV cell at current steps respectively.

Derating is a major factor that is applied to the PV array power output to account for reduced output in real-world operating conditions compared to the conditions under which the PV panel was rated. It is set at 80% in this paper. In actual operating conditions, factors such as soiling of the panels, wiring losses, shading, snowing cover, aging etc. all can have adverse effects on the performance of PV arrays [

Another significant factor that cannot be ignored is the temperature coefficient. The power output of the PV array can be affected by the cell temperature i.e. the surface temperature of the PV array. Usually, the higher the cell temperature, the lower would be the output of the PV array. The value of the temperature coefficient can be found in the product brochures which are provided by manufacturers.

The Kinetic battery model is a traditional representation of the operating condition of the battery [

In

The total amount of energy stored in the battery is defined as the sum of the available energy and bound energy,

Q_{1} and Q_{2} are the available power and the bound energy respectively.

Overall, two conditions are considered. When the power from the wind turbines and the PV module generators exceed the demand, the surplus will be stored in the batteries. When the power from the wind turbines and the PV module generator is not enough to support load, then energy will be extracted from the battery banks.

The nominal battery capacity can be modeled as below [

where N_{bt} is the total number of batteries, N_{bs} is the number of cells in series,

Another important factor that characterizes the battery behavior is the state of charge (SOC) of a battery. The state of charge at a time “t” can be obtained using the following equation [

P_{L}(t) is the electric power demand at time “t”, V_{b} is the battery voltage, C_{b} is the capacity of the battery bank, N_{wind} is the number of the wind turbine, and N_{PV} is the number of PV module.

If

If

A minimum state of charge of the battery bank (SOC_{min}) is also set in this paper to help expand the entire lifetime of the battery.

where DOD (%) is the maximum depth of discharge of the battery bank, a level of 60% is used in this paper; SOC_{r} is the rated state of charge of the battery bank, a level of 100% is used in this paper. As a result, the minimum state of charge of the battery bank would be 40% in this paper.

The net present cost is an important parameter used for comparison in different optimal type combinations of the micro-grid system [

The area is at the 34.5N latitude and 95.5E longitude locating in Qinghai Province, China where the sea level is above 4000 meters. Residents live sparsely, and most of them make a livelihood by farming and grazing. It is not uncommon that only several households can be seen around a ten kilometers radius. Different types of territories such as forests, deserts and wet meadows increase the difficulties for grid extension.

It is assumed that the peak load time is around 9 p.m. every day. And annual average load demand varies from 0.5 kWh/d to 2 kWh/d.

Data are obtained from the NASA Surface Meteorology and Solar Energy website. With the annual average solar radiation of 5.18 kWh/m^{2}/d, a figure providing monthly average solar monthly average solar radiation values and the curve represent clearness index over 22 years period is shown in

Three variables are studied in this case i.e. the annual capacity shortage (%), the annual average load demand (kWh/d) and the annual average wind speed (m/s). A capacity shortage is a shortfall that occurs between the required operating capacity and the actual amount of operating capacity the system can provide. In other words, the customers are willing to accept some unmet load in order to allow a smaller, less expensive power system. The total number of wind turbines is varied from 0 to 3. The total size of PV arrays varies from 0 kW to 0.4 kW. The total number of batteries is set from 0 to 8. All the total net present costs of different optimal system type combinations that satisfy the reliability required by

the hybrid system are calculated. An optimal system model with the least total net present cost would be the best solution in this case.

As is seen in

When the annual average wind speed increases to 6 m/s, the area of distribution of the best optimal system type changes, shown in

When the annual average wind speed increases to 7 m/s, the total net present cost of the best solution of the optimal system type of wind turbines, PV modules and batteries (AIR/PV/T-105) drops again compared with the value of the same point in

more, it is the first time that optimal system type of wind turbines and batteries (AIR/T-105) appears in the graph. When the annual capacity shortage is between 1.00% and 1.20%, the annual average load demand is between 0.8 kWh/d and 0.9 kWh/d, the best solution of the optimal system type is the combination of wind turbines and batteries (AIR/T-105). And it is predictable that when the annual average wind speed increases to more than 7 m/s, more areas in the graph would be covered by the optimal system type of wind turbines, PV modules and batteries (AIR/PV/T-105).

The wind/solar/battery micro-grid system is an alternative way to help serve power to rural areas where the electricity grid extension is not viable technologically or economically. A case study depended on the 22-years real data of Yushu, China is presented. Three variables are accounted i.e. the annual average wind speed (m/s), the annual average load demand (kWh/d) and the annual capacity shortage (%). A least total net present cost of all the optimal system type that meets the reliability of the microgrid would be the best solution. All the best solutions representing different optimal system types are shown in various graphs.

It is discovered that when both of the annual average wind speed and annual average load demand are lower than a particular point, the optimal system type would be the combination of PV modules and batteries. When either the annual average wind speed or the annual average load demand increases to a particular point, the optimal system type would be the combination of wind turbines, PV modules and batteries. When the annual average wind speed increases more and more, the optimal system type would be the combination of wind turbines and batteries.

Shao, Z.L. and Lo, K.L. (2017) A Case Study to Determine Optimal Capacity of a Stand-Alone Wind/ PV/Battery System. Energy and Power Engineering, 9, 300-307. https://doi.org/10.4236/epe.2017.94B035