Chukwuka G. Monyei

Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm

This paper examines the characterization of six oil wells and the allocation of gas considering limited and unlimited case scenario. Artificial gas lift involves injecting high-pressured gas from the surface into the producing fluid column through one or more subsurface valves set at predetermined depths. This improves recovery by reducing the bottom-hole pressure at which wells become uneconomical and are thus abandoned. This paper presents a successive application of modified artificial neural network (MANN) combined with a mild intrusive genetic algorithm (MIGA) to the oil well characteristics with promising results. This method helps to prevent the overallocation of gas to wells for recovery purposes while also maximizing oil production by ensuring that computed allocation configuration ensures maximum economic accrual. Results obtained show marked improvements in the allocation especially in terms of economic returns.

An integrated dynamic pricing scheme for improving the smartness of off grid distributed generation

This paper presents an integrated dynamic pricing scheme (iDPS) developed for an off grid community in sub-Sahara Africa (SSA). This proposed model employs a neighbourhood approach in effectively determining the electricity units due to connected consumers based on their monthly contributions. Assuming base values, this model obviates the need for real time inputs from the users owing to the high illiteracy level in SSA and dynamically computes electricity price in real time such that below par paying consumers pay more compared to base or above base consumers. Additionally, the smart load distribution board employed ensures that electricity units are dispatched in quantized values demanding greater energy efficiency from the consumers. This model achieves economic accrual which guarantees the sustainability of the off grid DG project.

An Improved Comfort Biased Smart Home Load Manager for Grid Connected Homes Under Direct Load Control

This paper presents an improved comfort biased smart home load manager (iCBSHLM) for grid connected residential houses. The proposed algorithm discriminates household loads into class 1 (air-conditioner, heating) and class 2 loads (dishwasher, cloth washer and cloth dryer) and achieves electricity consumption reduction and electricity cost reduction of up to 2.9% and 7.5% respectively using dynamic pricing (Price1) over time of use pricing (Price0), while ensuring that indoor temperature is kept within the user prescribed range and without any violation. iCBSHLM advances existing home energy management systems (HEMs) by ensuring that vulnerable household residents (especially the elderly) can still benefit from smart grid initiatives like HEMs without any discomfort. Furthermore, this research presents a simplistic model for heating, ventilation and cooling (HVAC) loads using capacitor charging/discharging behaviour.