The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.

Optimal sizing of power systems has a tremendous effective role in reducing the total system cost by preventing unneeded investment in installing unnecessary generating units. This paper presents an optimal sizing and planning strategy for a completely hybrid renewable energy power system in a remote Japanese island, which is composed of photovoltaic (PV), wind generators (WG), battery energy storage system (BESS), fuel cell (FC), seawater electrolysis plant, and hydrogen tank. Demand response programs are applied to overcome the performance variance of renewable energy systems (RESs) as they offer an efficient solution for many problems such as generation cost, high demand peak to average ratios, and assist grid reliability during peak load periods. Real-Time Pricing (RTP), which is deployed in this work, is one of the main price-based demand response groups used to regulate electricity consumption of consumers. Four case studies are considered to confirm the robustness and effectiveness of the proposed schemes. Mixed-Integer Linear Programming (MILP) is utilized to optimize the size of the system’s components to decrease the total system cost and maximize the profits at the same time.

The integration of renewable energy sources into the power grid poses several challenges due to their variability in output power as they primarily depend on weather conditions. As a countermeasure, battery energy storage systems are introduced in order to mitigate output power fluctuations of the renewable energy sources. Moreover, demand response programs have been attracting huge attention as an effective mechanism for efficient energy management. It enables power suppliers to cope with the output uncertainty of renewable energy systems. First step, we propose an HRES design methodology that takes into account one year. In order to find the optimal power supply configuration, it is necessary to take into account the changes in load demand due to weather conditions and seasons for one year. However, the computational cost of simulating one year is huge and requires a rather long simulation time. Therefore, we propose a simplified simulation method that uses clustering to account for changes in load demand due to weather and seasonality in one year. Second, the first proposed method is used to propose a method for optimizing the HRES equipment capacity and operation schedule considering demand response. In this paper, mixed-integer linear programing is used as the optimization method. The proposed method considers two types of demand response and achieves cost reduction by controlling load demand in response to generated power fluctuations.

Microgrid optimization is one of the most promising solutions to power system issues and new city electrification. This paper presents a strategy for optimal power scheduling of a residential microgrid depending on renewable generating sources and hydrogen power. Five scenarios of the microgrid are introduced to show the effect of using biomass energy and a seawater electrolyzer on microgrid cost and CO${}_2$ emissions. Time of use demand response is applied to reshape the electric load demand and decrease the dependence on grid power. The obtained results from the multi-objective optimization verify that biomass has a significant role in minimizing the cost and CO${}_2$ emissions; the cost is decreased by 37.9% when comparing scenarios with and without biomass. Besides, the FC integration with seawater electrolyzer and tanks reduces the microgrid emissions by around 40%.