Alireza Soleimani

Residential photovoltaic (PV) and battery systems operating under time-of-use tariffs create genuine opportunities for demand-side management, yet comparative evidence across strategies under realistic operating constraints remains scarce, and most reported gains rely on forecasting or optimization frameworks that are difficult to deploy on existing residential hardware. Three deterministic, rule-based strategies, namely peak shaving, load shifting, and valley filling, were evaluated using a verified, energy-conserving MATLAB (Version: R2024b), hourly simulation for a typical European prosumer (6 kWp PV, 6 kWh battery, approximately 6000 kWh annual consumption), incorporating realistic battery constraints and actual three-tier tariff structures across 8760 operational hours. Baseline operation without demand management achieved 31.7% self-consumption, 85.4% grid dependence, and an annual cost of €872. Load shifting produced the strongest economic outcome, reducing costs to €720 (€152 savings), with 36.1% self-consumption and 70.1% grid dependency. Peak shaving saved €126 through automated battery control alone, reaching 35.2% self-consumption without requiring behavioral change. Valley filling prioritized grid stability, lowering power fluctuations by 31%, while achieving 33.6% self-consumption and €112 in savings. No single strategy optimized all objectives simultaneously, and strategy selection should depend on prosumer priorities; the rule-based framework further offers a deployable performance reference against which future forecast- and optimization-based controllers can be assessed.

The convergence of additive manufacturing (AM) and artificial intelligence (AI) is poised to redefine the landscape of modern production; however, the literature remains fragmented across isolated applications, lacking a unified perspective on the engineering impact and practical deployment of these technologies. This review provides a comprehensive and critical synthesis of the state of the art in AI-enhanced AM, systematically covering supervised, unsupervised, and reinforcement learning paradigms, alongside deep-learning-based computer vision, natural language processing, and robotics. In contrast to prior works that focus on singular aspects, this paper consolidates progress across four core engineering domains: (i) lightweight and manufacturable design, (ii) real-time in situ defect detection and process analysis, (iii) energy-efficient process optimization, and (iv) cost-effective build-time estimation with intelligent support minimization. Beyond cataloging these advances, this review identifies key quantitative benchmarks and recurring technical challenges, including data scarcity, poor model generalizability, and the critical gap between offline prediction and real-time closed-loop control. To transcend these isolated successes and enable industrial adoption, we propose a novel, unified closed-loop AI-AM framework that tightly integrates generative design, process planning, in situ production monitoring, and continuous model updating into a cohesive digital thread. Furthermore, a domain-stratified SWOT analysis is compiled, offering a strategic evaluation of strengths, weaknesses, opportunities, and threats across the four application pillars. By bridging the gap between laboratory prototypes and production-ready autonomous systems, this review serves as a definitive reference for researchers and practitioners aiming to navigate, deploy, and advance the rapidly evolving field of AI in additive manufacturing.

The rapid growth in global energy demand, driven by urbanization, industrial expansion, and electrification, poses critical challenges to the stability, flexibility, and sustainability of modern energy systems. Simultaneously, the increasing integration of non-dispatchable renewable energy sources introduces significant variability and intermittency, threatening grid reliability. Energy storage systems (ESS) have emerged as a key enabler in addressing these issues by decoupling generation and consumption, enhancing grid stability, and facilitating higher penetration of renewable energy. This chapter provides a comprehensive overview of ESS technologies, including electrochemical, mechanical, thermal, and hydrogen-based systems, alongside their technical characteristics, maturity levels, carbon footprint, and economic considerations. It compares short-term versus long-term storage solutions and evaluates their strategic roles in supporting daily operations, seasonal balancing, and sector coupling. The chapter also highlights global trends in energy consumption, the challenges of renewable integration, and the need for hybrid storage architectures. Ultimately, it demonstrates that ESS is not merely a technical solution but a systemic pillar for enabling a resilient, low-carbon, and equitable energy transition.

Distribution network reconfiguration stands out as a potent approach for reducing power losses in radial distribution feeders, where losses notably are more those observed in transmission systems. The distribution power losses incurred not only lead to costly operations but also contribute to a compromised voltage profile in electric energy grids. Habitual reconfiguration involves adjusting the form of the distribution grid to minimize electric energy losses, with a focus on the electricity demanded by end-consumers. The existing models predominantly address distribution network reconfiguration without accounting for the different types of load demand linked to points of consumption, each exerting distinct power loss effects in contrast to single-type demands. Despite the imperative load type diversity role, most models neglect this aspect. The limited studies incorporating load type into reconfiguration strategies tend to employ non-linear formulations, introducing complexities. While models with non-linear characteristics may be tackled via metaheuristic techniques, they fall short of guaranteeing optimal solutions. Alternatively, solving such models using solvers for non-linear equations in conventional tools of optimization demands computations that require significant time. To address these challenges, the present article proposes an impressive model for reconfiguring distribution networks that consider different load types. Notably, this model is designed for seamless implementation with linear procedures. Our study’s results demonstrate the exactitude of the introduced model in yielding accurate results for targeted reconfiguration challenges, coupled with the advantage of swift execution suitable for real-time reconfigurations.

Carbon trading is a market-based technique to decrease greenhouse gas (GHG) emissions through the sale and purchase of carbon offsets. Incorporating artificial intelligence (AI) into carbon trading can alter the industry by improving information processing, statistical modeling, and trade automation. This paper presents an extensive structure for AI-driven carbon trading that considers critical aspects such as carbon trading volume and pricing to maximize productivity and sustainability. The study assesses numerous AI and machine learning (ML) theories, including their use in cost prediction, real-time market forecasting, and financial risk assessment. The main results show that AI integration increases market transparency, lowers fraud, and promotes informed decision-making, all of which helps to establish an environmentally friendly, effective, and adaptable carbon market. Furthermore, this work underscores the role of AI in advancing carbon-neutral economies by fostering innovation in emissions monitoring and reporting. These advancements highlight AI's critical contribution to achieving global climate objectives and addressing the urgent challenges posed by climate change.