Sweden plans to decarbonize its energy sector by 2045 through initiatives such as electrification of transport & industry, wind power expansion, HYBRIT and increased use of biomass. Hitherto studies have predominantly focused on electricity sector. Nevertheless, the targets for 2045 necessitates studying the Swedish energy system at national scale in the context of sector coupling & storage. This work examines the role of thermal energy storage (TES) and hydrogen storage (HS) in the future energy system with high proportions of wind power. Three scenarios SWE_2045, NFF_2045 and RES_100 representing three different energy systems were simulated in EnergyPLAN modelling tool, incorporating TES, HS and sector integration. The results indicate that both TES and HS can improve flexibility of the system by enhancing wind integration. Heat pumps (HPs) coupled with TES can increase wind integration by 5–9% and also reduce the operation of thermal boilers and CHP, resulting in total fuel reduction by 2–3%, depending on the scenario. However, HS is not a viable option for storing excess electricity alone, as shown in SWE_2045 since it does not facilitate additional wind integration. It demonstrates better outcome mainly when there is a significant demand for hydrogen in the system, resulting in wind integration of 6–9%. However, HS does not contribute to the reduction in total fuel since it does not have an impact on the fuel input in district heating sector.

Increasing PV penetration in the residential sector has led to supply demand mismatch in PV in the electricity market, specially during the peak demand hours and peak PV generation hours. Smart grid and smart meters have opened up avenues for designing data driven methodologies to optimize the generation and consumption of energy. In this paper, a dynamic load control mechanism is designed which optimizes the operation of individual appliances (heat pump, electric boiler, battery storage, solar PV and electric car). The optimization algorithm utilizes rolling horizon approach to consider the real time load control. A case of an individual house in Helsinki, Finland is considered to test the developed method. The results of dynamic load control mechanism were compared with operational optimization, wherein dynamic control is not implemented with different building classification and electricity contracts. From the results, it is observed that the optimization with a longer duration offers more benefits as compared to real time control mechanism, but does not reflect a real world scenario. Additionally, consumers having electricity contracts which are variable had the most savings and provides the highest flexibility to the electricity system. 

The adoption of electric boats faces challenges due to range anxiety. This study addresses this challenge by optimizing the allocation of slow and fast chargers for public electric boat charging stations. A mixed-integer linear programming model was developed to minimize charging infrastructure and electricity costs while meeting the demand of each participating marina in Stockholm, Sweden. The model identified three distinct categories of stations based on the optimal charger allocation: stations prioritizing fast chargers (e.g., Sickla station with 13 fast and 8 slow chargers for 208 boats), stations prioritizing slow chargers (e.g., Kungsholmen station with 2 slow and 3 fast chargers for 50 boats), and stations with an equal number of both charger types (Äppelviken station with 2 each for 25 boats). This study found out that all stations will achieve financial viability within seven years, indicating a promising return on investment. This study also proposes a novel way for optimizing charger allocation in electric boat charging infrastructure, promoting wider electric boat adoption.

This study develops a an optimization model focused on the layout and dispatch of a low-carbon hydrogen supply chain. The objective is to identify the lowest Levelized Cost of Hydrogen for a given demand. The model considers various elements, including electricity supply from the local grid and renewable sources (photovoltaic and wind), alongside hydrogen production, compression, storage, and transportation to end users. Applied to an industrial case study in Sweden, the findings indicate that the major cost components are linked to electricity generation and investment in electrolyzers, with the LCOH reaching 5.2 EUR/kgH2 under typical demand conditions. Under scenarios with higher peak demands and greater demand volatility, the LCOH increases to 6.8 EUR/kgH2 due to the need for additional renewable energy capacity. These results highlight the critical impact of electricity availability and demand fluctuations on the LCOH, emphasizing the complex interdependencies within the hydrogen supply chain. This study provides valuable insights into the feasibility and cost-effectiveness of adopting hydrogen as an energy carrier for renewable electricity in the context of decarbonizing industrial processes in the energy system.

Reinforcement Learning for EV charging optimization has gained significant academic attention in recent years, due to its ability to handle uncertainty, non-linearity, and real-time problem-solving. While the number of articles published on the matter has surged, the number of open-source environments for EV charging optimization remains small, and a research gap still exists when it comes to customizable frameworks for commercial vehicle fleets. To bridge the gap between research and real-world deployment of RL-based charging optimization, this paper introduces FleetRL as the first customizable RL environment for fleet charging optimization. Researchers and fleet operators can easily adapt the framework to fit their use-cases, and assess the impact of RL-based charging on economic feasibility, battery degradation, and operations.

The large-scale penetration of electric vehicles (EV) in road transport brings a challenging task to ensure the balance between supply and demand from urban districts. EVs, being shiftable loads can provide system flexibility. This work investigates the potential role of smart charging of EVs in mitigating the impact of the integration of a mix of residential and public EV charging infrastructure on power networks. Furthermore, the impact of integrating solar photo-voltaic (PV) and battery energy storage systems (BESS) has been explored where BESS improves PV self-consumption and helps in peak shaving during peak load hours. Annual losses, transformer congestion, and cost of electricity import assessment are detailed by considering the power network of Stockholm as a case study. Smart charging with loss-optimal and cost-optimal charging strategies are compared to uncoordinated charging. The cost-optimal charging strategy is more favorable as compared to the loss-optimal charging strategy as it provides more incentives to the DSOs. The loss-optimal charging strategy reduces 35.5 % of losses in the network can be reduced while the cost-optimal solution provides a 4.3 % reduction in the electricity cost. The combined implementation of smart charging, PV, and BESS considerably improves energy and economic performance and is more effective than EV smart charging alone.

Goa is the smallest state in India, covering 3700 km2 Its unique location makes it an ideal focal point for state-specific analyses representing a small-scale version of India’s diverse energy landscape. There is a lack of local power capacity, and the state primarily relies on centrally allocated power stations dominated by 572 MW of coal, constituting 73 % of the total allocated capacity. Despite advancements in electrification, fossil fuels remain the primary energy source in sectors like cooking, industry, and transportation, with around 36 PJ or 72 % of the total energy supplied. This study presents targeted strategies for achieving 100 % renewable energy deployment by conducting a sectoral analysis and emphasizing temporal resolution. Leveraging open-source models like OSeMOSYS-pulp enhances transparency and accessibility in energy planning. At the same time, stakeholder engagement ensures alignment with local priorities. The findings highlight opportunities for Goa to transition to renewable energy sources, including green electricity generation and imports, alongside policy measures such as Renewable Purchase Obligations (RPOs) and long-term Power Purchase Agreements (PPAs) incentivizing hybrid systems with battery storage. The study also emphasizes the importance of transitioning traditional cooking technologies to cleaner options like biogas and electric cooking for universal clean cooking, thus reducing energy consumption from 6.4 PJ to 2.4 PJ by 2050. Moreover, it proposes electrifying various passenger transport modes, reducing emissions, and lowering final energy consumption from around 20 PJ to 10 PJ by 2050. The study demonstrates the impact of increasing temporal resolution on energy planning by better capturing demand variability and load patterns. This results in a decreased solar installation of around 1.6 GW by 2050. Finally, this study provides insights for sustainable energy transition tailored to local contexts like Goa and similar regions with limited renewable potential.

Hydrogen is envisioned to become a fundamental energy vector for the decarbonization of energy systems. Two key factors that will define the success of hydrogen are its sustainability and competitiveness with alternative solutions. One of the many challenges for the proliferation of hydrogen is the creation of a sustainable supply chain. In this study, a methodology aimed at assessing the economic feasibility of holistic hydrogen supply chains is developed. Based on the designed methodology, a tool which calculates the levelized cost of hydrogen for the different stages of its supply chain: production, transmission & distribution, storage and conversion is proposed. Each stage is evaluated individually, combining relevant technical and economic notions such as learning curves and scaling factors. Subsequently, the findings from each stage are combined to assess the entire supply chain as a whole. The tool is then applied to evaluate case studies of various supply chains, including large-scale remote and small-scale distributed green hydrogen supply chains, as well as conventional steam methane reforming coupled with carbon capture and storage technologies. The results show that both green hydrogen supply chains and conventional methods can achieve a competitive LCOH of around €4/kg in 2030. However, the key contribution of this study is the development of the tool, which provides a foundation for a comprehensive evaluation of hydrogen supply chains that can be continuously improved through the inputs of additional users and further research on one or more of the interconnected stages.

In the transition to sustainable energy, hydrogen emerges as a crucial carrier, addressing the intermittency of renewable sources. However, its storage poses challenges due to its unique properties, notably in surface-based systems. Underground hydrogen storage (UHS) presents a viable solution, drawing on natural gas storage methodologies while offering enhanced capacity, safety, and reduced environmental impact. This chapter provides an in-depth analysis of UHS, exploring its operational mechanisms, geological requirements, and the adaptation of existing infrastructures. It highlights the significance of geological formations such as caverns, aquifers, and depleted hydrocarbon reservoirs, evaluating their suitability based on various factors including rock and mineral properties as well as the relevant operational dynamics. Moreover, the chapter provides an overview that integrates scientific, engineering, and logistical perspectives of UHS, offering insights into project planning and the global context of UHS initiatives, thereby underscoring its potential and challenges as a key component in the energy transition.

The transition towards a circular economy necessitates the development of tools for evaluating circularity. This paper proposes an approach with three important indicators—industrial waste, energy use, and IT material waste—to solve the challenge of evaluating circularity in the Swedish automobile industry. Each indicator includes a number of scores and sub-scores allowing for a thorough evaluation of circularity at product level. Qualitative and quantitative data are gathered to calculate scores for individual aspects of the indicators, which are then combined into an overall product score using a weighted average, and associated with an accuracy measurement reflecting the quality of the input data. The sensitivity of circularity indicator values to data availability underscores the importance of accuracy metrics, while placing a priority on simplicity, usefulness, and adaptability. This framework incorporates knowledge from scientific literature, business reports, and sustainability specialists. The result is an overall product circularity score. Such a scorecard can be crucial in accelerating the transition to circularity and sustainability since it highlights areas that need improvement. The framework's development incorporates feedback from sustainability experts, while interviews with industry professionals and data testing ensure its practicality for companies. The framework is designed to be adaptable and scalable to suit industry requirements. Companies are encouraged to adopt it as an internal tool and take ownership to drive circularity initiatives effectively.