Abstract
Climate change, geopolitical tensions, and decarbonization targets are bringing the resilience of the European electric power system to the forefront of discussion. Among various regulatory and technological solutions, voluntary demand response can help balance generation and demand during periods of energy scarcity or renewable energy generation surplus. This work presents an open data service called Interoperable Recommender that leverages publicly accessible data to calculate a country-specific operational balancing risk, providing actionable recommendations to empower citizens toward adaptive energy consumption, considering interconnections and local grid constraints. Using semantic interoperability, it enables third- party services to enhance energy management and customize applications to consumers. Real-world pilots in Portugal, Greece, and Croatia with over 300 consumers demonstrated the effectiveness of providing signals across diverse contexts. For instance, in Portugal, 7% of the hours included actionable recommendations, and metering data revealed a consumption decrease of 4% during periods when consumers were requested to lower consumption.

Abstract
The footwear industry is energy-intensive and, consequently, a source of large amounts of greenhouse gas emissions every year. Issues related to climate change and growing conflicts on a global scale that impact the prices of raw materials and energy prices have led companies in the footwear industry to take actions to mitigate these impacts. Among these actions is the growing focus on producing its energy from energy systems based on renewable sources and battery energy storage units. This paper addresses the energy-efficient manufacturing scheduling in footwear industries with onsite energy production from a photovoltaic system with batteries. The problem is formulated as a mixed integer linear programming problem. Different objectives are presented, depending on the priorities of the entity that owns the footwear factory, namely, minimizing operation costs, minimizing CO2 emissions, or both. The case study is footwear factory located in Portugal that uses a manufacturing process based on injection molding. The results show the effectiveness of the proposed approach, with active demand side management playing a fundamental role in shifting periods of higher energy consumption to periods of lower prices or lower CO2 emissions. Also, Pareto fronts are depicted to make the trade-off between CO2 emissions and operation costs. As expected, the reduction of CO2 emissions promotes an increase on operation costs. Furthermore, a sensitivity analysis is carried out on the increase in photovoltaic capacity and battery capacity. The results show that increasing photovoltaic and battery capacity promotes reductions in costs up to 30% and in the emissions up to 37%.

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
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
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
Traditionally, proportional-integral (PI) control has ensured the successful application of automatic generation control (AGC). Two design features of AGC-PI are the following: (1) it is merely a reactive system which does not take full advantage of existing knowledge about the system and (2) the control signal sent to all units is divided proportionally to their participation in the AGC. These two features ensure simplicity and, thus, reliability for the regular functioning of the power system. However, when the power system is recovering from a loss of generation, such features can become shortcomings. This paper proposes a model predictive control (MPC) to improve performance of AGC in such a scenario. The contrast with the traditional approach is as follows: instead of using merely two system measures which are also the control objectives (frequency and interconnection flow), the proposed controller relies on an internal model that takes advantage of further known variables of the power system, especifically the ramping capabilities of participating units. While still respecting the participation factors, it is shown that the proposed model allows to exhaust earlier the availability of faster units, such as some demand response, as the one to be provided by hydrogen electrolysers, and thus reestablishes the frequency and interconnection flows in a faster way than typical AGC-PI.