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This chapter demonstrates a biogas renewable energy resource potential study for electric power generation from easily available biogas feedstock materials in four selected case study sites. Under this study, the site used in the model is a rural Kebele in Jama Woreda at 10.548°N, 39.33°E. The common biogas feedstocks considered under this study are animal slurry, human feces and jatropha byproducts whereas the biodiesel is considered from jatropha seed.

One of the main challenges of the future in the utility sector is constructing the new transmission line corridor. This is due to the fact that land compensation cost associated with the expansion of a new transmission line corridor becomes very expensive and also power transmission efficency is very low. In addition to that, the high carbon emission, which is the major challenge of the world right now, related to the additional conventional energy-based power generation to meet dramatically increased electricity demand and the volatility nature of the existing transmission networks are some of the main drivers to implement FACTS controller in transmission network for flexible, reliable, efficient and stable power transmission. This study accounts modeling of static VAR compensator (SVC), static synchronous compensator (STATCOM), unified power flow controller (UPFC) in a 5-bus transmission system to enhance transmission efficency and the quality of power supplied to the costomer. FACTS devices for improving the transmission line capacity and voltage profile of the power system. The three FACTS controllers are modeled for the standard 5 bus IEEE system based on Newton Raphson algorithm using NEPLAN simulation software in order to investigate their impacts on transmission line capacity and voltage profile improvement. Based on the simulation result, the voltage profile as well as the capacity of the IEEE 5 bus system is improved well by using each of the FACTS controller. From the simulation result we can conclude that the STATCOM and SVC are very efficent in voltge profile improvement whereas the UPFC is well performed for the power transmission capability of the transmission network.

Load demand is highly stochastic and uncertain. This is because it was highly influenced by a number of variables like load type, weather conditions, time of day, the seasonality factor, economic constraints, and other randomness effects. The loads are categorized as holiday loads (national and religious), weekdays, and weekend days. The nonlinearity and uncertain characteristics of electrical load in a microgrid are one of the major sources of power quality problems in a microgrid system, and they can be handled using an accurate load forecast model. The fuzzy load prediction model can effectively handle these nonlinearity and uncertainty characteristics to have an accurate load forecast, but the main challenge with this model is its inability to accommodate a large volume of historical load and weather information when the membership function of the input and output fuzzy variables and the number of the fuzzy rules are tremendous. The swarm intelligence load forecast model based on particle swarm optimization algorithms can improve the limitations of the fuzzy system and increase its forecasting performance. The parameters of time, temperature, historical load, and error correction factors are considered as the Fuzzy and Fuzzy-PSO model input variables, while the forecasted industrial load is the only output variable. The Gaussian membership function is considered for both the input and output fuzzy variables. A 3-year historical hourly load data of an Ethiopian industrial system is used to train and validate both prediction models. The mean absolute percentage error (MAPE) is used to evaluate the performance of these prediction models. The Fuzzy-PSO load prediction model shows results that have superior performance to the fuzzy-alone load prediction results.

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