Groundwater quality, vulnerability and potential assessment in Kobo Valley development project, Ethiopia

This study deals with investigating the groundwater quality for irrigation purpose, the vulnerability of the aquifer system to pollution and also the aquifer potential for sustainable water resources development in Kobo Valley development project. The groundwater quality is evaluated up on predicting the best possible distribution of hydrogeochemicals using geostatistical method and comparing them with the water quality guidelines given for the purpose of irrigation. The hydro geochemical parameters considered are SAR, EC, TDS, Cl-, Na+, Ca++, SO4 2- and HCO3 -. The spatial variability map reveals that these parameters falls under safe, moderate and severe or increasing problems. In order to present it clearly, the aggregated Water Quality Index (WQI) map is constructed using Weighted Arithmetic Mean method. It is found that Kobo-Gerbi sub basin is suffered from bad water quality for the irrigation purpose. Waja Golesha sub-basin has moderate and Hormat Golena is the better sub basin in terms of water quality. The groundwater vulnerability assessment of the study area is made using the GOD rating system. It is found that the whole area is experiencing moderate to high risk of vulnerability and it is a good warning for proper management of the resource. The high risks of vulnerability are noticed in Hormat Golena and Waja Golesha sub basins. The aquifer potential of the study area is obtained using weighted overlay analysis and 73.3% of the total area is a good site for future water well development. The rest 26.7% of the area is not considered as a good site for spotting groundwater wells. Most of this area fall under Kobo-Gerbi sub basin.

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches.