Abstract
Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this article: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as 'active' actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Abstract

Abstract
AbstractRenewable energy-based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long-term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen-based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting-edge methodologies, such as machine learning-based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen-based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Abstract
Carbon neutralization is a European concern, which is why the maritime sector should implement strategies to reduce greenhouse gas (GHG) emissions, particularly in port areas. The Port of Sines, a very important maritime hub in Portugal, is in a stage of significant expansion including new terminal constructions and renewable energy projects, which amplify its energy demands. This paper presents a new approached of a load model, consumption Integration of renewable energy sources and energy storage systems for the Port of Sines, analysing a global hourly energy consumption in two months of 2023. Using a Software Package MATLAB, the details of the consumption profiles of all ships and terminals in order to identify periods of peak demand, the information on the integration of renewable energy sources and energy storage systems, will be studied and analysed. Due to the increase in maritime traffic and the use of potential Onshore Power Supply Systems (OPS) to reduce emissions, in this study a new energy requirements will be analysed. This new model will be as a step for optimizing the port's electrical infrastructure, enhancing energy efficiency, and supporting sustainable growth. Finally, some conclusions that provide a valuable contribution to the understanding of the Portuguese Ports, aims to provide a critical study of the load model to be taken, into account when managing port energy demand and advancing environmental goals are pointed out.

Abstract
In a world that is in constant changing and where carbon neutrality becomes a common objective, it is necessary to implement European policies and targets to reduce greenhouse gas emissions. The maritime sector is one of the most polluting in the world, becoming mandatory to implement technologies in port area to reduce their footprint. Most of the good's transportation are made by sea, the maritime industry is growing, and the biggest chair of greenhouse gas emission comes from shipping. The seaport has the role to implement solutions to reduce the emissions in port area, allowing the ships to shutdown their engines while they are moored in port. Renewable energy production alongside with shore power supply systems are becoming a common solution in ports as some of the technologies that allows to reduce ships emissions in port area. This paper presents the state of the art of onshore power supply in ports and standards related to shore power supply and data requirements for load model built and emissions calculations.

Abstract
A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.