No abstract

Due to the depletion of traditional energy resources, emissions of greenhouse gases, climate change, etc., renewable energy resources (RER) based power generation is becoming the main source of the present and future power sector. The major RERs, including solar, wind, and small hydro, may provide reliable and sustainable solutions in the smart grid environment. Solar and wind energy-based power generation is more prevalent but varies in nature and is not even very predictable very efficiently. Therefore, it has become necessary to integrate two or more RER and develop a hybrid energy system (HES). The HESs provide a cost-effective and reliable power supply with reduced and/or almost negligible greenhouse gas emissions as well. Due to economic and power reliability concerns, the optimal sizing of components is necessary for the development of an optimum HES. In recent years, metaheuristic evolutionary algorithms have been widely used for optimal sizing of HES. Harris hawk’s optimizer (HHO) is a recently devised metaheuristics search method that has the ability to discover global minima and maxima. However, due to its weak exploitation capacity, the basic HHO algorithm’s local search is pretty slow and has a slow rate of convergence. Thus, to boost the exploitation phase of HHO, a new approach, random exploratory search centered Harris hawk’s optimizer (hHHO-ES), has been developed in the present work for optimal sizing of HES. The suggested approach is validated and compared to existing optimization approaches for a variety of well-known benchmark functions, including unimodal, multimodal, and fixed dimensions. Following this, it is used to develop HES, which will be capable of providing power to remote areas where grid supply is scarce. The objective function is formulated using net present cost (NPC) as a prime function under a set of constraints such as bounds of system components and reliability. The obtained results are compared with those from harmony search (HS) and particle swarm optimization (PSO) and found to be better.

In this paper, an intelligent energy management system for the smart home that combines the solar energy as well as the energy from the battery storage devices has been proposed to reduce the dependency on the power grid and make the system to be more economical. The proposed system manages the energy requirement of the smart home by properly rescheduling and arranging the power flow between the energy storage devices, grid power, and the photovoltaics. The power grid can absorb the excess power from the designed system whenever the load requirement is low, and on the other hand, it can supply the power to the load in case of peak demand. Therefore, in the designed system, a user has the flexibility to sell the extra power for the purpose of revenue. A thorough simulation of the system has been carried out, and the results obtained show the effectiveness of the approach in terms of energy management between the different sources.

This study evaluates the hydropower potential in the design of a micro-hydro/solar photovoltaic hybrid system with battery energy storage for increasing the access to electricity in Ikukwa Village in Mbeya Region of Tanzania. Usually, hybridized hydropower schemes are designed from perennial streams for the provision of electricity. This study incorporates the run-of-the river (COE) power scheme, which originates from the untapped potential of nonperennial hydro-energy source and the use of traditional approach of data measurements for Ikata tributary to design hybrid system. The system is optimized by the minimization of the total net present cost (NPC) and cost of energy (COE) using the soft computing method of Hybrid Optimization of Multiple Energy Resources (HOMER) software and artificial intelligent (AI) techniques. AI optimization techniques such as particle swarm optimization (PSO), grey wolf optimization (GWO), and GWO-PSO hybrid (GWO-PSOHD) algorithms have been employed for further optimal results. The data for solar radiation and the tributary have been obtained from the National Aeronautics and Space Administration (NASA) and traditional methods of measurements, respectively. The estimated maximum water flow rate and head are 2.943 m3/s and 13 m, respectively. In the same period, the approximated theoretical power potential of the tributary is found to be 375 kW. Total NPCs obtained from HOMER, PSO, GWO, and GWO-PSOHD methods are $ 141, 397.76, $ 95 167.21, $ 92 472.82, and $ 91,854.10, respectively. Similarly, the optimal results of COE from HOMER, PSO, GWO, and GWO-PSOHD approaches are $ 0.1818/kWh, $ 0.1185/kWh, $ 0.1182/kWh, and $ 0.1181/kWh, respectively. Comparatively, PSO implementation has indicated the greatest energy cost, while the cost acquired by GWO-PSOHD is the lowest for all aforementioned AI optimization techniques. The tributary under study has a high potential of diversification of energy sources for rural electrification in the area of study and other parts of the world with comparable conditions.