A thorough and exhaustive review of relevant literature and associated works is carried out to critically examine energy poverty in Nigeria with respect to ownership and income. Using the desktop approach and empirical formulas, the persistent failure of public infrastructure like healthcare, education and security to the poor electricity generation, transmission and distribution capacity in the country is examined; alongside current government contribution to buoying our generation capacity and electricity access through policies and investment. The findings of the review reveal the urgent need for the smart roll out of distributed generation units in order to stimulate and encourage the ongoing diversification of the economy and also the need for a sustainable road map that incorporates the successes of countries faced with similar challenges. This review paper also proposes the need for palliatives in form of subsidized solar home systems (SHSs) through a sustainable and economically viable means for off grid homes to assuage the effects of non-availability of grid electricity.

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.

This research paper presents the development of a biased load manager home energy management system for low-cost residential building occupants. As a smart grid framework, the proposed load manager coordinates the operation of the inverter system of a low cost residential apartment consisting of rooftop solar photovoltaic panels, converter and battery, and provides a platform for discriminating residential loads into on-grid and off-grid supply classes while maximizing solar irradiance for optimum battery charging and improving consumer comfort from base levels. Modelled in a Matlab simulation environment, the system incorporates a converter system for maximum power point tracking using a hopping algorithm, with a dedicated mechanism for smart dispatch of specified loads to meet the users' comfort based on the priority ranking of the loads. Results obtained indicate a 34% reduction in electricity cost, 26% reduction in carbon emissions and a 4% increase in comfort level for the photovoltaic/battery/utility option compared to the utility only option. The results further show that cost is a major factor affecting the users' comfort and not necessarily dispatch of appliances to meet energy needs. The research can be useful for encouraging the adoption of the photovoltaic/battery/utility option by low/middle income energy users in developing countries.

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