This study analyses the potential of renewable energy to reduce inflationary pressures arising from energy imports in Turkiye. Annual data for the period 1980-2022 are used in the analysis. In this study, unit root properties are examined using the Zivot-Andrews and Lee-Strazicich tests, both of which explicitly account for structural breaks. Cointegration is investigated via the Johansen and Hatemi-J cointegration tests. Long-run coefficients are subsequently estimated using the DOLS and FMOLS estimators. The robustness of the empirical findings is further assessed using the ARDL approach. In addition, an interaction term is constructed to measure the impact of renewable energy in alleviating inflationary pressures arising from energy imports. The results show that energy imports and exchange rate have an increasing impact on inflation, while renewable energy and the interaction term have a decreasing impact. DOLS, FMOLS, and ARDL results support each other. Moreover, in both models, the impact of renewable energy in mitigating inflationary pressures stemming from energy imports is stronger than the direct disinflationary impact of renewable energy.
This paper proposes a novel computationally efficient algorithm for optimal sizing of Battery Energy Storage Systems (BESS) considering renewable energy bidding strategies. Unlike existing two-stage methods, our algorithm enables the cooptimization of both by updating the BESS size during the training of the bidding policy, leveraging an extended reinforcement learning (RL) framework inspired by advancements in embodied cognition. By integrating the Deep Recurrent Q-Network (DRQN) with a distributed RL framework, the proposed algorithm effectively manages uncertainties in renewable generation and market prices while enabling parallel computation for efficiently handling long-term data.
In the 21st century, transitioning to renewable energy sources is imperative, with fossil fuel reserves depleting rapidly and recognizing critical environmental issues such as climate change, air pollution, water pollution, and habitat destruction. Embracing renewable energy is not only an environmental necessity but also a strategic move with multiple benefits. By shifting to renewable energy sources and supporting their production through the acquisition of renewable energy certificates, we foster innovation and drive economic growth in the renewable energy sector. This, in turn, reduces greenhouse gas emissions, aligning with global efforts to mitigate climate change. Additionally, renewable energy certificates ensure compliance with regulations that mandate the use of renewable energy, enhancing legal adherence while promoting transparency and trust in energy sourcing. To monitor the uptake of renewable energy, governments have implemented Renewable Energy Certificates (RECs) as a tracking mechanism for the production and consumption of renewable energy. However, there are two main challenges to the existing REC schema: 1) The RECs have not been globally adopted due to inconsis
This paper introduces the Future Atmospheric Conditions Training System (FACTS), a novel platform that advances climate resilience education through place-based, adaptive learning experiences. FACTS combines real-time atmospheric data collected by IoT sensors with curated resources from a Knowledge Base to dynamically generate localized learning challenges. Learner responses are analyzed by a Generative AI powered server, which delivers personalized feedback and adaptive support. Results from a user evaluation indicate that participants found the system both easy to use and effective for building knowledge related to climate resilience. These findings suggest that integrating IoT and Generative AI into atmospherically adaptive learning technologies holds significant promise for enhancing educational engagement and fostering climate awareness.
This paper develops a geospatial framework for climate risk stress testing in California with applications to banking and climate-exposed sectors such as agriculture, real estate, and tourism. The study integrates physical hazard mapping, sector-specific exposure analysis, and scenario-based financial risk assessment to evaluate how wildfires, drought, flooding, extreme heat, and transition risks may affect regional economic activity and financial stability. The framework is intended to support portfolio monitoring, climate scenario analysis, and institutional readiness under emerging disclosure and risk-management standards. In addition, the paper provides a survey-based implementation guide for benchmarking current climate-risk practices and data needs across industry and academic stakeholders.
The integration of power electronics-based energy storage systems (PEESs) into power systems introduces potential instabilities. This study reviews efforts in dynamic analysis of both AC and DC power systems integrated with PEESs, covering dynamic modeling, analysis methods, and potential instability risks. Major conclusions are drawn as: 1) Simplified models of PEESs have been widely used for dynamic analysis of power systems. However, it may cause "error aggregation" as the scale of PEESs increases, leading to mistakes in results, which induces significant concerns. 2) Traditional stability mechanism analysis methods remain effective for single grid-connected PEES and large-scale PEESs with parallel and series connections. However, they are inadequate for PEESs with distributed connections. To fill in this gap, an idea of mechanism analysis based on "dynamic reconstruction" is proposed. 3) Potential instability risks caused by PEESs integration may differ from those caused by renewable energy integration due to differences in functional controls and bidirectional power flow. However, comprehensive investigations in this regard are lacking and require significant attention. To ens
The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.