Smart consumers and prosumers play a key role in the modern power and energy systems; due to significant share of self-consumption, they may reduce the burden on local distribution systems or microgrids; moreover, as a large share of their demand is supplied by their own renewable energy resources, they considerably contribute to the decarbonization targets. Identifying the impact of smart prosumers on microgrids may assist decision makers to find the challenges and make suitable changes. The operation of reconfigurable microgrids with high penetration of green smart homes (SHs), charging stations (CSs) and hydrogen fueling stations (HFSs) has not been addressed in the literature, so, this paper aims to propose a framework for energy management in the mentioned smart consumers/prosumers and investigate their impact on host microgrids. In the proposed framework, firstly, all electric vehicles (EVs) and fuel cell vehicles (FCVs) optimize their own charging schedule using the price signals received through vehicle-to-infrastructure technology; in the second level, each HFS, CS or SH solves its own energy management model and in the third level, the operator of the microgrid solves its day-ahead operational planning model. The solvers of General Algebraic modeling Systems (GAMS) are used to solve all the mentioned models. The results confirm the efficiency of the developed multi-level methodology; according to the results, the studied microgrid enjoys a daily profit of $571.47, meaning that the revenue, earned by selling electricity to CSs, SHs, HFSs and its own demands is considerably higher than sum of the cost of its micro-turbines and the cost of purchased electricity from upstream grid. The results indicate that the batteries decrease the daily cost of smart homes by 4 %; moreover, the results suggest that batteries cause drastic change in operation cost of the microgrid.

The increasing frequency of environmental events driven by global warming poses a significant threat to smart network operations, highlighting the need for advanced self-healing techniques to enhance grid stability and accelerate recovery during emergencies. This study proposes a two-stage distributed optimization mechanism for self-healing in coupled electricity and gas networks. The mechanism leverages the capabilities of smart prosumers, such as industrial parks, charging stations, and power-to-hydrogen (P2H) units, to minimize load shedding and bolster resilience under emergency conditions. In the first stage, the distribution system operator optimally reconfigures electricity and gas networks, plans distribution feeder operations, and deploys fuel cell-equipped trucks to allocate the required power and gas capacities to smart prosumers via signal pulses. The second stage focuses on modeling the smart prosumers, enabling them to offer their available capacities to the network operator in response to these signals. To ensure secure convergence with minimal information exchange between the two stages, an augmented Alternating Direction Method of Multipliers (ADMM) algorithm is utilized. The proposed mechanism was validated on two different test systems, solved using the GUROBI solver within GAMS. Simulation results demonstrate that the mechanism effectively harnesses maximum capacities from smart prosumers, reducing load shedding by 64.08 % and improving the resilience index by 80.34 %. Furthermore, the augmented ADMM enhanced computational efficiency, achieving a 45.3 % faster solution compared to the standard version while ensuring global optimality.

In the field of optimization problems, the optimization of energy systems problems is of significant importance, mainly due to their dramatic role in achieving sustainability. The complexity of energy systems optimization problems, intense constraints, and various decision variables have led many researchers to utilize meta-heuristics optimization algorithms to optimize such issues and improve energy systems. Meta-heuristic algorithms that can find global solutions and prevent trapping in local optima can efficiently solve energy systems problems. Grey Wolf Optimizer (GWO), one of the well-known meta-heuristic optimizers inspired by the grouped hunting process of wolves, has been employed in different studies to deal with energy systems optimization problems. GWO has received much attention in the literature due to its proper exploratory and exploitative features, rapid and mature convergence rate, and simplicity in design and coding. This paper reviews various GWO applications for tackling optimization problems related to production, conversion, transmission and distribution, storage, and energy consumption. It is highly believed that this paper can be a practical and innovative reference for researchers, professionals, and engineers.