Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. This proposal is devised to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe Index (WI) and the Higher Heating Value (HHV)) within admissible limits. The model has been applied to a gas network case study with three distinct scenarios and implemented using Python. The findings from the case study show the maximum permissible volume of hydrogen in the network, quantify the total savings in natural gas, and estimate the reduction in carbon dioxide emissions. © 2023 IEEE.

Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. The model is designed to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe index (WI) and higher heating value (HHV)) within admissible limits. This study also presents the maximum injection allowable of hydrogen correlated with the gas quality index variation. The model has been applied to a case study of a gas network with four distinct scenarios and implemented using Python. The findings of the case study quantify the maximum permitted volume of hydrogen in the network, the total savings in natural gas, and the reduction in carbon dioxide emissions. Lastly, a sensitivity analysis of injected hydrogen as a function of the Wobbe index (WI) and Higher Heating Value (HHV) limits relaxation.

Abstract
As the global community transitions towards decarbonization and sustainable energy, green hydrogen is emerging as a key clean energy carrier. This paper addresses the role of hydrogen in transportation, emphasizing the European Union's additionality principle for renewable energy sources in green hydrogen production. It introduces a model for optimally designing hydrogen fueling stations, considering electrolyzers, hydrogen storage, fuel cells, PV systems, and batteries. This model also considers the participation in electricity (energy and secondary reserve), hydrogen, and oxygen markets, and it is evaluated under different additionality policy scenarios. Results indicate that stricter additionality policies reduce the internal rate of return. However, participation in secondary reserve markets significantly boosts operational revenues and compensates for higher investment costs.

Abstract
Green ammonia production stands as a pivotal component in the transition towards sustainable energy and agriculture, poised to revolutionize numerous industries. This paper presents an optimization control framework for industrial green ammonia fuel hubs to engage in electricity, hydrogen, and oxygen markets, addressing both economic and technical considerations. By evaluating scenarios with and without battery storage, this study demonstrates the potential for increased profitability and energy independence through secondary reserve market participation, alongside insights into the economic viability of photovoltaic investments. These findings underscore the importance of considering market dynamics and technological integration in the sustainable operation of green ammonia production hubs.

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 stochastic optimization model for industrial hubs to enable their participation in energy markets. The model aims to leverage the resources of multi-energy systems to minimize energy costs in the day-ahead market. It accounts for uncertainties in photovoltaic generation, electrical and heat demand, and outdoor temperatures. A comparison is made with a deterministic approach, along with an analysis of the impact of thermal storage and reserve market participation on costs and bidding strategies. The results show that the stochastic approach is more conservative than the deterministic, while the integration of thermal storage and reserve services help decrease costs.