OPTIMAL SIZING OF GRID CONNECTED MICROGRID IN RURAL AREA OF PAKISTAN WITH WIND TURBINES AND ENERGY STORAGE SYSTEM USING PARTICLE SWARM OPTIMIZATION

Pakistan has been riddled with energy shortage crisis. Long hours of load shedding have caused major economic setbacks in urban areas and rural areas do not even make the cut. Some rural parts, which are connected to the grid, suffer major load shedding and so economic growth is minimal. Most energy is directed towards industrial demand; hence the domestic demand suffers and causes long hours of load shedding. To aid this supply-demand gap, microgrids can be helpful in relieving some of the domestic load on the grid. A microgrid may be more economical only as a support for the main grid in an area, depending on its configuration. Since microgrids are generally composed of renewable energy sources like wind or solar or a combination of both, the supply from just these sources may result in high intermittency. To allow uniform supply, a backup energy source or energy storage is included with the renewable sources. Sizing a microgrid for the targeted region is critical. Some major sizing factors include the availability of renewable resource, load profile of the region, land availability, grid availability, etc. For this thesis, a region near Gharo, a town in Thatta District in Sindh, Pakistan, is selected to deploy the microgrid with a wind farm and battery energy storage system. The microgrid is connected to the main feeder, which supplies grid electricity to a small town of 30 small homes, a school and a small hospital. Hourly wind speed data and an annual load profile is used to calculate the most economic size of the microgrid, depending on the energy dispatch philosophy. To find the most economical solution, this thesis incorporates a stochastic technique, known as the Particle Swarm Optimization (PSO), which is a powerful intelligence evolution algorithm for solving optimization problems. Over the years, PSO has gained popularity due to its simple structure and high performance in solving linear or non-linear objective functions with any number of constraints. In this case, the objective function to be minimized is the net present cost of the microgrid, which comprises of annual capital cost, annual operation and maintenance cost, annual replacement cost of all equipment involved and the annual net cost of buying/selling electricity from/to the grid, respectively