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.

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.

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.

This article presents a four-level hierarchical model to incorporate decentralized energy communities (ECs) into local electricity markets. The model utilizes an innovative distribution system operator (DSO)-driven algorithm to maximize grid services from ECs, monetize their energy surplus, and adapt market exchanges to network security constraints. In level 1, EC members determine their internal scheduling and power exchanges. A decentralized peer-to-peer (P2P) structure embedded in level 1 enables power sharing with dynamic pricing and limited data sharing among EC members. Levels 2 and 3 involve the EC operators and the retailer company determining their market strategies. In level 4, a DSO-driven algorithm is deployed to evaluate security constraints and the feasibility of exchanges between market players. Implemented on a modified 594-node distribution network in Victoria, Australia, the model optimally integrates ECs with local electricity markets. By preserving agents' privacy and keeping exchange details confidential, the proposed model ensures next-day contracts adhere to network security restrictions, maximizes grid services from ECs, and reduces members' electricity bills by 7.5%.

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.

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.

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.