Abstract
Switched reluctance generators (SRG) are one of the machines with huge potential in wind power generation due to their reliability and robust design. However, the inherent characteristics of SRGs lead to significant challenges in achieving high efficiency and low output current and torque ripple simultaneously. The performance of SRGs is hindered by conflicting requirements. To address these issues, this chapter presents an optimization control strategy aimed at improving the static performance of SRGs. The chapter discusses the application of the Particle Swarm Optimization (PSO) technique to optimize the commutation angles, specifically the turn-on (?on) and turn-off (?off) angles, for an 8/6 SRG. The proposed strategy consists of two main steps. First, a Maximum Power Point Tracking (MPPT) algorithm is implemented to maximize power output at varying rotor speeds, combined with a direct power control method to regulate the power generated by the SRG. Second, a multi-objective function is developed to evaluate the SRG performance, considering key factors such as power output, output current ripple, and torque ripple. The simulation results indicate that implementing optimized turn-on and turn-off angles leads to a reduction in torque ripple from -1.78 Nm using the conventional technique to -0.66 Nm with the proposed method, corresponding to an impressive 63% decrease. Furthermore, the optimization strategy effectively maximizes the efficiency of the system employing an MPPT approach, ensuring optimal energy conversion under varying operating conditions. Future research directions include experimental validation of the proposed control system on real hardware to assess its practical feasibility and performance under real-world operating conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The wine production, included in the primary sector is a great cultural and economic deal, both nationally and internationally matters. However, it is highly dependent on natural resources, and traditionally involves high energy and water consumption. Given the global climate change scenario and the need for efficient resource management, it is necessary to implement a sustainable plan for the wine sector to realize sustainable practices. Data from the International Organization of Vine and Wine (OIV), states that global wine production exceeded 260 million hectoliters, in 2022. These has resulted in significant water and energy consumption, with around 500-1200 m(3) of water used per hectare for irrigation and 1.2 gigajoules per hectoliter of wine produced, concluding that more than 80% of total water consumption is associated with irrigation, while more than 90% of energy consumption, is associated with winery processes. In this context, the scarcity of water or the need to achieve carbon neutrality by 2050 makes it essential to adopt energy and water efficiency measures that allow for the sustainable management of resources without endangering the sector's viability. With this in mind, a case study applied to a Portuguese wine industry is presented, including data analysis from water and energy consumption. Also, efficiency metrics will be analyzed, proposing management and decision-support tools based on monitoring and sensor-based techniques. In fact, one example of these efficiency measures deals with the adoption of systems that provide real-time data on consumption patterns and resource availability in order to improve sustainability of the global process production.

Abstract
This study provides a comprehensive review of how game theory can be applied to model and optimize dynamic coalitions in contemporary energy markets. With the increasing decentralization of energy systems driven by technologies such as solar photovoltaics, home energy storage, and electric vehicles, consumers have begun to play a more active and influential role in the market. In this new context, where cooperative and collective decision-making is gaining importance, game theory emerges as a valuable tool for analyzing and structuring these interactions. The primary objective of this work is to systematically review existing models, assess their methodological strengths and limitations, and identify open research gaps that hinder their applicability to real-world settings. By synthesizing the current state-of-the-art, this study aims to highlight pathways toward the development of more realistic and effective models that capture the dynamic and interdependent behaviors of energy consumers and the coalitions they form. Ultimately, this review seeks to provide an updated overview of this growing field, serving both as a basis for future research and as a foundation for the design of solutions that promote fairer, more efficient, and more participatory energy markets, especially for small-scale consumers, who now have greater voice and power of choice. © 2026 IEEE.

Abstract
The present work proposes a two-stage optimization approach for flexibility services provided by battery energy storage systems (BESS) in distribution networks with photovoltaic (PV) generation and electric vehicles (EV). The considered flexibility services include reserve allocation and voltage regulation to support network operation. The first stage optimizes the day-ahead (DA) scheduling of distributed BESS to minimize overall costs, including energy, BESS usage, and reserve, while accounting for stochastic variations in load, PV generation, and EV penetration. The second stage simulates the real-time (RT) operation of the electrical distribution network, evaluating system behavior under different scenarios based on DA decisions. A coordinated control strategy is applied, integrating DA scheduling with network voltage levels. Deviations between BESS outputs in DA and RT stages are fed back into a new DA run to adjust outputs and reduce costs. Results on a medium-voltage distribution system with 157 nodes (based on a reduced version of the EPRI CKT5 feeder) demonstrate that the proposed scenario-based model provides feasible solutions under uncertainty, with BESS playing a key role while strictly adhering to planned operational modes from DA to RT, as typically enforced in energy market participation.

Abstract
Seaport cranes operate continuously and consume large amounts of energy while aiming to minimise containerships' berthing time. Although previous studies have contributed to addressing the crane scheduling problem, most have focused exclusively on loading time, often overlooking the aspect of energy consumption. Furthermore, crane activity is typically modelled in a simplified manner-commonly assuming a fixed cycle duration or constant energy usage when handling a container-without accounting for the impact of variable container masses. In this study, an energy-aware quay crane scheduling formulation for container terminals is proposed, highlighting the importance of integrating an energy model into the scheduling problem. The optimisation problem is formulated as a Mixed Integer Linear Programming (MILP) model. The objective is to minimise total energy costs by reordering the sequence in which containers are handled, while respecting precedence constraints defined by the ship's stowage plan. Two solution methods-a MILP approach solved using CPLEX and a genetic algorithm (GA)-are compared. The results indicate that, for larger containerships, the genetic algorithm provides a more efficient solution method. Moreover, incorporating detailed energy consumption models for electric cranes may significantly reduce energy costs during containership handling operations.

Abstract
The increasing energy demand in seaport operations, driven by electrification and decarbonisation targets, requires enhanced tools for operational planning and flexibility management. This paper proposes a novel centralised Energy Management System designed for seaports, which, unlike previous approaches that mainly focused on cost minimisation jointly optimises Battery Energy Storage System scheduling, energy and reserve market participation, and carbon-intensity reduction. A key contribution of this work is the integration of CO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document} emission forecasts and day-ahead market data into a multi-objective formulation, allowing the Energy Management System not only to minimise operational costs but also to reduce indirect emissions. Additionally, a Traffic Light system is proposed to support operators' decision-making by providing actionable flexibility guidelines. A case study based on real-world data from the Port of Sines shows that this method achieves at least an 17% reduction on an annual basis compared to baseline operations, while ensuring cost efficiency. Results highlight the Energy Management System's potential as a decision-support tool for port authorities seeking to align operational efficiency with sustainability goals.