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This paper introduces a benchmark comparison of distributed optimization algorithms based on dual decomposition for model predictive control problems with constraint coupling. Such problems can be viewed as multiple subsystems linked through constraints on shared, limited resources. In a dual decomposition approach, the allocation and use of these resources are coordinated by repeatedly calculating their associated prices and distributing those values to the participating subsystems. After a short overview of model predictive control, various architectures and communication structures suitable for distributed implementations are outlined. The centralized control problem is then decomposed into several subproblems by introducing dual variables. A range of distributed optimization methods is examined, including the recently developed quasi-Newton dual ascent algorithm. Additionally, an epigraph reformulation of the bundle, combined with a line search technique, is proposed for the quasi-Newton dual ascent method; these enhancements improve its numerical stability and accelerate convergence relative to the earlier trust-region approach. Lastly, the quasi-Newton dual ascent algorithm is benchmarked against the subgradient method, the bundle trust method, and the alternating direction method of multipliers across a broad set of test problems. All benchmark instances are openly accessible on GitHub. 

This paper introduces a novel hybrid artificial intelligence technique designed to enhance energy management efficiency within smart grids, with a particular focus on Renewable Energy Communities (RECs). The proposed approach integrates a Time Delay Neural Network (TDNN) to predict upcoming values of key energy variables in the community. These forecasts then feed into a stochastic Model Predictive Control (MPC) framework, which optimizes community operations through effective Battery Energy Storage System (BESS) control strategies. Evaluation on a public dataset for a 24-hour prediction horizon yielded Mean Absolute Errors (MAE) of 1.60 kW for photovoltaic generation, 2.15 kW for total energy consumption, and 0.30 kW for common services. When integrated with stochastic MPC, these predictions yielded higher financial returns than those from alternative methods. Overall, the system increased total income by 18.72% compared to the same management approach without any forecasting component.