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This paper presents an improved optimization algorithm for the energy management of a renewable energy solar/wind microgrid with multiple diesel generators applied to off-grid remote communities. The main objective aims to solve the economic emission dispatch problem with a price penalty factor to minimize the energy cost and the emission level. An enhanced metaheuristic optimization algorithm, Lévy arithmetic algorithm, is applied to improve the searchability for optimal solution compared to the conventional arithmetic algorithm. The Lévy arithmetic method is used for the management of the microgrid and compared to other metaheuristic optimization algorithms for the same application. Comparative analysis demonstrates good cost savings using the Lévy arithmetic algorithm, compared to other optimization algorithms such as the arithmetic algorithm, crow search algorithm, hybrid modified grey wolf algorithm, interior search algorithm, cuckoo search algorithm, particle swarm algorithm, colony algorithm, and genetic algorithm. • Proposed an energy management algorithm for the PV/Wind/Diesel Microgrid in off-grid remote communities. • Introduced an enhanced arithmetic optimization approach for addressing economic and emission dispatch. • Evaluated the optimization method's effectiveness and comparative study between the LAA and AOA. • Compared the optimized results of proposed method with other well-known optimization techniques. [ABSTRACT FROM AUTHOR]

• An autonomous water pumping system (WPS) is suggested. • A hybrid energy system is optimized through seagull optimization algorithm (SOA). • The impacts of load management (LM) strategy in system sizing is analyzed. • Techno-economic evaluation in the presence and absence of load management is made. This study introduces an innovative irrigation system tailored for a tomato field in Algeria, optimizing its elements using Seagull Optimization Algorithm (SOA) over a 20-year lifespan. The system integrates photovoltaic (PV), wind turbine (WT), diesel generator (DG), battery, and tank storage for water pumping. The novelty lies in the incorporation of Load Management (LM), creating a dynamic water demand profile across four tomato development stages. Without LM, the optimal configuration yields a Net Total Present Cost (NTPC) of $14,140, corresponding to a Cost of Energy (COE) of 0.292 $/kWh. This configuration ensures a 24% Loss of Power Supply Probability (LPSP), a 41.2 % Loss of Water Supply Probability (LWSP), and an emission factor of 0.169 kg/kWh during 279 h of DG operation annually. With LM, the COE drops to $0.298/kWh, reducing the NTPC to $12,080. This LM strategy gives an 18 % LPSP, 20 % LWSP, and an Energy Fraction (EF) of 0.140 kg/kWh during 168 h of DG operation annually. The present application is a promising and cost-effective solution for optimizing Water Pumping Systems (WPS) in agricultural settings. [ABSTRACT FROM AUTHOR]