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This work aims to optimize the economic dispatch problem of a microgrid system in order to cover the load of a commercial building in Algeria. The analyzed microgrid system is connected to the power grid and composed of photovoltaic panels (PV), wind turbine, battery energy storage system (BESS) and diesel generator. To ensure energy balance and the flow of energy, we have implemented an energy management strategy based on Marine Predator Algorithm (MPA) and Multilayer Perceptron Neural Network (MLPNN), which guarantee an optimal economic operation of the system. First, using historical meteorological data, the power generation is forecasted a day-ahead using MLPNN, which allows the optimization of the microgrid operation. Second, the proposed strategy has been studied under three different microgrid configurations. Eventually, the performances of MPA are compared against well-known algorithms. The results indicate that the integration of the PV-BESS microgrid system significantly reduces the daily operating cost up to 34.5%. Due to the availability of wind resources in the studied area, the addition of a wind turbine to the microgrid minimizes the operating cost by 43.96% compared to the operating cost of the power grid. In the case of selling excess energy to the main power grid, the operating cost could be decreased as much as 49.33%. [ABSTRACT FROM AUTHOR]

• Energy management strategy is proposed for a hybrid pumped hydro-battery storage. • Optimal system sizing and sensitivity analysis are carried out. • Hybrid storage has electricity cost 3.5 times lower compared to single storage. • Hybrid storage reduces curtailment by 290% compared to single storage. • Hybrid storage has smaller upper reservoir hence greater applicability potential. The development of energy storage systems paves the way towards a high integration of renewable energy sources in the electricity generation sector. Considering above, this paper introduces a new energy management strategy to efficiently coordinate a hybrid energy storage system based on pumped hydro storage (long term bulk storage) with batteries (short term, more flexible). For the purpose of this analysis, hourly time series of irradiation, wind speed, temperature and real measured load (characteristic for farmstead) covering one year were gathered for the selected site in Algeria. The optimal size of the system is determined based on a multi-objective optimization using a grey wolf optimizer implemented in MATLAB software. The results indicate that the hybrid storage system enables achieving higher reliability at lower cost in comparison to a system with single storage technology. The use of hybrid storage also reduces the curtailment of renewable generation. Further findings reveal that the cost of an optimal energy supply system with 97.5% reliability is 0.162 €/kWh, 0.207 €/kWh and 1.462 €/kWh for hybrid storage, battery and pumped storage, respectively. However, sensitivity analysis shows that the optimal hybrid storage configuration is less resilient when changes in irradiation/temperature/load are considered. This indicates that special actions (upscale of installed power) must be undertaken to avoid lower performance of hybrid storage systems. In summary, the hybrid storage system seems to be better sized for consideration in optimized solar/wind conditions, but by avoiding oversizing they are less resilient to future potential changes in renewable energy availability. [ABSTRACT FROM AUTHOR]