Filipe Joel Soares

Abstract
This chapter explores the potential of thermal energy storage (TES) systems towards the decarbonization of industry and energy networks, considering its coordinated management with electrochemical energy storage and renewable energy sources (RES). It covers various TES technologies, including sensible heat storage (SHS), latent heat storage (LHS), and thermochemical energy storage (TCS), each offering unique benefits and facing specific challenges. The integration of TES into industrial parks is highlighted, showing how these systems can optimize energy manage-ment and reduce reliance on external sources. A district heating use case also demonstrates the economic and environmental advantages of a multi-energy management strategy over single-energy approaches. Overall, TES technologies are presented as a promising pathway to greater energy effi-ciency and sustainability in industrial processes.

Abstract
This paper proposes a planning optimization model to help distribution system operators (DSOs) decide on the most cost-effective investments to handle the wholesale market participation of distributed energy resources (DERs). Two investment options are contemplated: market redesign; and network augmentation. The market redesign is employed through a DSO framework used to coordinate the network-secure participation of DERs in wholesale markets. Network augmentation is achieved by investing in new HV/MV OLTC and MV/LV transformers. To evaluate the performance of our planning model, we used the IEEE 69-bus network with three DER aggregators operating under different DER scenarios. Our tests show that the planning problem suggests investment decisions that can help DSOs guarantee network security. Market redesign has shown to be the most cost-effective option. However, this option is not always viable, namely in scenarios where not enough DERs are available to provide network support services. In such scenarios, hybrid investment solutions are required.

Abstract
Currently, the transmission system operators (TSOs) from Portugal and Spain do not procure a frequency containment reserve (FCR) through market mechanisms. In this context, a virtual power plant (VPP) that aggregates sources, such as wind and solar power and hydrogen electrolyzers (HEs), would benefit from future participation in this ancillary service market. The methodology proposed in this paper allows for quantifying the revenues of a VPP that aggregates wind and solar power and HEs, considering the opportunity costs of these units when reserving power for FCR participation. The results were produced using real data from past FCR market sessions. Using market data from 2022, a VPP that aggregates half of the HEs and is expected to be connected in the country by 2025 will have revenues over EUR 800k, of which EUR 90k will be HEs revenues.

Abstract
This paper presents a methodology for assessing the long-term economic feasibility of renewable energy-based systems for green hydrogen and ammonia production. A key innovation of this approach is the incorporation of a predictive algorithm that optimizes day-ahead system operation on an hourly basis, aiming to maximize profit. By integrating this feature, the methodology accounts for forecasting errors, leading to a more realistic economic evaluation. The selected case study integrates wind and PV as renewable energy sources, supplying an electrolyser and a Haber-Bosch ammonia production plant. Additionally, all supporting equipment, including an air separation unit for nitrogen production, compressors, and hydrogen / nitrogen / ammonia storage devices, is also considered. Furthermore, an electrochemical battery is included, allowing for an increased electrolyser load factor and smoother operating regimes. The results demonstrate the effectiveness of the proposed methodology, providing valuable insights and performance indicators for this type of energy systems, enabling informed decision-making by investors and stakeholders.

Abstract
Reducing the gap between renewable energy needs and supply is crucial to achieve sustainable growth. Hydroelectric power production predictions in several Madeira Island catchment regions are shown in this article using Long Short-Term Memory, LSTM, networks. In order to foresee hydro reservoirs inflows, our models take into account the island's dynamic precipitation and flow rates and simplify the process of water moving from the cloud to the turbine. The model developed for the Socorridos Faja Rodrigues system demonstrates the proficiency of LSTMs in capturing the unexpected flow behavior through its low RMSE. When it comes to energy planning, the model built for the CTIII Paul Velho system gives useful information despite its lower accuracy when it comes to anticipating problems.