Smart prosumers with Distributed Generation (DGs) and controllable loads can provide cost-effective grid services. However, realizing this potential requires distributed optimization mechanisms that ensure market efficiency, participant privacy, and compliance with electricity market regulations. This paper presents a bi-level distributed optimization mechanism to maximize flexibility services from industrial parks and Internet Data Centers (IDCs) in distribution-level Congestion Management (CM) markets. The upper-level models the Distribution System Operator (DSO), which identifies congested lines using linear AC power flow analysis on pre-settled energy market results and sends corrective signals to prosumers. The lower level allows prosumers to adjust their operations accordingly and communicate updated transactions back to the DSO. A novel proxy-driven algorithm is proposed to facilitate service-sharing among geo-distributed IDCs, considering congestion issues. Additionally, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm enables decentralized coordination among market agents, achieving 74.52 % faster convergence than the standard ADMM. A real-world case study from Spain demonstrates that the proposed mechanism enables the grid operator to maximize grid services from prosumers, reducing congestion alleviation costs by 35.27 %. Moreover, IDCs reduced daily costs by 11.07 % through service-sharing and task-shifting aligned with CM market signals, while industrial parks achieved a 13.68 % cost reduction by aligning material production processes with CM market signals, both enabled by the proposed bi-level mechanism.

Energy hubs are emerging as key enablers of flexibility in modern multi-energy systems, particularly as the integration of energy storage technologies expands across residential, commercial, and industrial sectors. Their ability to coordinate distributed resources, including storage, demand response, and vehicle-to-grid (V2G) assets, offers a scalable pathway to support system reliability and local balancing needs. Realizing this potential, however, requires decentralized coordination schemes that allow hubs and system operators to collaborate without extensive data exchange, a growing necessity in competitive and privacy-sensitive environments. This study develops a decentralized bi-level optimization framework that explicitly links the operational decisions of energy hubs with the real-time management of coupled electricity and gas networks. At the upper level, the system operator supervises both infrastructures, addresses energy imbalances, and specifies the flexibility requirements to be procured from upstream networks and downstream hubs. Intra-day uncertainties in demand and renewable generation are modeled using white noise with Gaussian, Beta, and Weibull probability distributions to capture realistic operational fluctuations. At the lower level, diverse hubs autonomously schedule their storage units, controllable loads, and V2G resources to maximize the flexible services they can offer. To enhance coordination performance, an adaptive Alternating Direction Method of Multipliers (ADMM) is introduced, which reduces communication exchanges and achieves a 63.27% faster convergence compared to the classical formulation. The proposed framework is implemented in GAMS and solved with GUROBI, using a test system that couples a 118-bus electrical distribution network with a 65-node gas distribution network. Results demonstrate that the model effectively activates multi-energy flexibility from integrated demand response, energy storage systems, and electric vehicles. This coordinated flexibility increases economic benefits for hubs, reduces the system operator's operational costs by up to 19.33%, and lowers total system losses by 6.12%.

Microgrids equipped with distributed renewable energy resources, energy storage systems, controllable loads, and vehicle-to-grid (V2G) technologies have emerged as critical enablers for sustainable energy transitions. However, their effective integration into day-ahead electricity markets requires advanced decentralized coordination mechanisms that ensure both robust scheduling under uncertainty and the preservation of agent privacy. Hence, this paper introduces a two-layer optimization framework to coordinate residential and industrial microgrids with the distribution network operator (DNO), addressing economic, technical, and environmental objectives. The proposed model employs an enhanced alternating direction method of multipliers (ADMM) algorithm, which dynamically adjusts power exchange prices based on carbon tax rates. This approach incentivizes low-carbon operations while preserving the confidentiality of internal microgrid schedules. Residential and industrial microgrids perform day-ahead scheduling in the first layer by leveraging the flexible capacities of their diverse technologies. Subsequently, they submit their desired exchange plans for participation in the day-ahead electricity market to the DNO in the second layer, where the feasibility of their implementation is evaluated. The model was validated on a 123-bus electricity distribution network comprising 32 residential microgrids and 17 industrial microgrids. The results demonstrated its effectiveness, achieving a 12.19 % reduction in carbon emissions and a 11.24 % decrease in operational costs. Furthermore, the proposed enhanced ADMM reduced convergence time by 43.16 % compared to the standard ADMM, significantly expediting coordination among decentralized agents in day-ahead markets.

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 growing integration of renewable energy sources (RES) into power grids has introduced significant operational variability, amplifying the need for robust flexibility solutions to maintain grid reliability. Demand-side resources, such as flexible loads and electric vehicle (EV) fleets, present cost-effective avenues for balancing supply and demand dynamics. This study proposes a decentralized bi-level optimization framework to enhance the utilization of demand-side flexibility and energy storage systems while ensuring market participant privacy. A Virtual Storage Plant (VSP) model is introduced to coordinate distributed energy storage assets under the supervision of the Transmission System Operator (TSO). The upper-level problem represents the TSO's strategic planning, while the lower-level problem addresses the operation of VSPs, EV parking facilities, and flexible loads. To optimize market interactions and minimize information exchange between the TSO and service providers, an adaptive Alternating Direction Method of Multipliers (ADMM) is employed. The proposed framework is validated using a 30-bus power transmission system, solved through the GUROBI solver within the GAMS environment. The results indicate an 18.7 % reduction in energy balancing costs and a 12 % decrease in transmission losses, alongside a 60 % improvement in convergence speed, demonstrating enhanced coordination, cost efficiency, and privacy preservation.

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.