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This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts. • Bi-level energy management strategy for optimal operation of grid tied microgrids. • Load shifting approach that reshapes the load according to the target load profile. • A novel real-time scheduling strategy of grid tied microgrids under uncertainties. [ABSTRACT FROM AUTHOR]

• Reactive power management. • Point estimation method. • Hony Badger algorithm. • Two-stage stochastic programming. • Inverter reactive power capability. In order to reduce the Static Var Compensators' (SVCs') initial investment costs and their anticipated yearly running costs, this paper offers a two-layer methodology for the allocation and sizing of the SVCs in radial distribution systems. First, the Power Loss (PLI) is used to choose the best candidate buses for allocating SVCs. A two-stage stochastic programming algorithm is then proposed to solve the sizing problem, with the Point Estimate Method (PEM) being used to account for the uncertainties in the PV and load powers. Analysis is also done on the advantages of combining the SVCs and the PV inverter's ability to provide reactive power. The optimization problem is solved using Honey Badger Algorithm (HBA) and compared to Archimedes Optimization Algorithm (AOA), Beetle antenna based Grey Wolf Optimization (BGWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The IEEE 33 and 69 radial bus systems have been used to validate the suggested methodology. The findings demonstrate that, HBA models delivered the best outcomes in terms of convergence characteristics and computation time. When considering the PV inverter's capability to provide reactive power, estimated yearly operating costs are reduced by 17.44 % and 36.36 % for compensations of 600 kVAR and 1,000 kVAR, respectively. [ABSTRACT FROM AUTHOR]