Authors:V. Shahbazbegian, H. Ameli, G. Strbac, H. Laaksonen, and M. Shafie-khah
Abstract:
In sustainable energy transitions, the utilization of hydrogen is crucial, providing flexibility in the operation of net-zero emission renewable-based energy systems. This paper presents a study on the optimal operation of net-zero emission multi-energy future microgrids that utilize hydrogen as an alternative fuel instead of natural gas. The electrolyzers’ output is injected into the hydrogen grid to meet demand or converted back to electricity later using generating units, owing to the storage capability of pipes, called linepack. For this purpose, a detailed mathematical model is developed to simulate the main characteristics of grids (e.g., voltage, current, hydrogen flow, and pressure) as well as various components (e.g., renewable systems, electrolyzers, and hydrogen-fired units). To become more realistic, a possibilistic-robust approach is developed to account for the uncertainty arising from the lack of real-world implementation. By representing a case study, a test is performed to evaluate the possibility of employing a low-pressure gas grid to meet the demand for hydrogen. After that, the effects of electrolyzers are analyzed in the presence and absence of the uncertainty consideration approach. The result indicates that, despite hydrogen’s lower energy density compared to natural gas, it is still feasible to satisfy the same energy demand level, considering the technical characteristics of the grid. The integration of electrolyzers can reduce wind curtailment by 2 % and supplement hydrogen demand by 50 %. A higher level of conservatism in the possibilistic-robust approach leads to an increase in the mean value of the objective function and a reduction in the standard deviation under the realization of uncertain parameters, which provides the decision-makers with a more realistic insight
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Authors:Cameron Aldren, Nilay Shah, Adam Hawkes
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Deriving accurate cost projections associated with producing hydrogen within the context of an energy-export paradigm is a challenging feat due to non-deterministic nature of weather systems. Many research efforts employ deterministic models to estimate costs, which could be biased by the innate ability of these models to see the future. To this end we present the findings of a multistage stochastic model of hydrogen production for energy export (using liquid hydrogen or ammonia as energy vectors), the findings of which are compared to that of a deterministic programme. Our modelling found that the deterministic model consistently underestimated the price relative to the non-deterministic approach by $ 0.08 0.10 kg-1(H2) (when exposed to the exact same amount of weather data) and saw a standard deviation 40% higher when modelling the same time horizon. In addition to comparing modelling paradigms, different grid-operating strategies were explored in their ability to mitigate three critical co-sensitive factors of the production facility: high-cost hydrogen storage, uncertainty in weather forecasting and sluggish production processes. We found that a grid-wheeling strategy substantially reduces the production cost for a solar system (by 16% and 21% for LH2 and NH3, respectively) due to its ability to guarantee the return of energy borrowed overnight during the day, but was not effective for the wind system, due to the non-periodic nature of aeolian weather patterns.
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Authors:Chandreyee Mallick, Maria Khalid, Asif Ali Tahir, Mohammad Abusara, Aritra Ghosch, Tapas Kumar Mallick, Yusuf Nabado Chanchangi
Abstract:
Rising global demand for sustainable energy and food has driven the integration of agriculture with innovative
renewable technologies, creating a transformative approach to meet these vital needs. It explores the synergies
between agrivoltaics (AV), semi-transparent photovoltaics (STPV), and green hydrogen production, highlighting
their potential in sustainable development. This review acknowledges and illustrates the concepts, potential
benefits, and challenges of individual technologies (AV, STPV and hydrogen electrolysis). This study presents a
critical analysis on a global scale, showing that spatial heterogeneity significantly impacts crop growth, yield,
and quality, which vary greatly by species and environmental conditions. Combining these efficient technologies
results in complete and effective sustainable systems that offer enhanced benefits, such as improved resource use
efficiency, increased resilience to climate change, and higher overall productivity in agricultural practices.
Advancements in AV engineering can significantly bolster agricultural productivity, environmental protection,
and economic sustainability, making these integrated systems increasingly viable and attractive options for the
global agricultural community. Therefore, the review emphasises the urgent need for expanded studies on a
diverse range of crops, especially in semiarid regions where water conservation is paramount and highlights the
importance of adopting standardised metrics for consistent evaluation.
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Authors: Zoya Pourmirza, Benoit Couraud Mehmet Bozdal, Sonam Norbu, Jamie Blanche, Clara Crivellaro, Dlzar Al Kez, Merlinda Andoni, Mela Bettega, Ahmad Taha, Valentin Robu, Sara Louise Walker, Aoife Foley, David Flynn
Abstract:
The transition to a decarbonised energy sector necessitates the integration of diverse energy carriers into a cohesive multi-energy system. This integration extends beyond physical infrastructure to include cyber layers that enable secure and efficient cross-vector operations. However, the expansion of cyber-physical systems (CPS) in energy networks introduces challenges related to cybersecurity, data governance, operational control, system resilience, real-time synchronisation, and interoperability. This paper presents a comprehensive review of research studies and industrial initiatives on CPS for multi-energy systems from 2019 to 2024, highlighting major technical approaches proposed by the academic community. Key innovations include the deployment of AI-based algorithms for forecasting, optimisation, and addressing cybersecurity threats in energy systems. Other key developments include the use of digital twins for enhanced monitoring and management of renewable energy assets, as well as predictive maintenance, and the integration of digital capabilities into the operational control of energy systems. The review also details state estimation techniques and cybersecurity approaches to mitigate risks associated with large-scale CPS expansion. Additionally, this study examines how CPS has begun to integrate social aspects, including end-user preferences and societal impacts, into the operations of cyber-physical energy systems. This is supported by a novel standardised cyber-physical-social energy systems (CPSES) architecture, which extends the Smart Grid Architecture Model (SGAM) to provide a structured framework for social multi-energy systems. This study serves as a critical resource for researchers, policymakers, and industry stakeholders aiming to advance the integration of digital technologies and CPS in the energy transition.
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Authors: Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper,
Abstract:
The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting and uncertainty calibration using diffusion and score-based approaches, subject to dataset and evaluation setup. GenAI supports system planning through synthetic scenario generation, enhances operational decision support and demand response coordination, and contributes to reliability, cybersecurity, and market analysis. LLMs primarily function as language-driven decision support and knowledge integration components across multiple application domains. Despite computational and data-related constraints, GenAI represents an important enabler of the sustainable digital transition by supporting resilience, adaptability, and governance in renewable energy systems.
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Authors: Dingxuan Li, Yiji Lu
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The application of heat pump systems is of great significance for energy consumption and emissions reduction. This article investigates a novel flexible heat pump system and conducts a comprehensive energy, economic, and environmental assessment of it. By analysing meteorological data of Glasgow, a typical heat load curve is established, and the study is conducted based on this curve, with comparisons made to a baseline two-stage heat pump system. Energy analysis shows that the flexible heat pump system achieves its maximum SCOP improvement at 8 °C, with increases of 14.06% for R134a and 11.05% for R1234yf compared with the baseline system. Economic assessments indicate that, despite higher initial investment costs, the flexible system’s lifecycle cost (LCC) is lower than that of the baseline system, with savings of approximately £1011 and £776 for R134a and R1234yf, respectively, and payback periods of approximately 10.5 years and 11.0 years. Regarding environmental analysis, the flexible system lowers lifecycle CO2 emissions for both refrigerants, with the greatest reductions achieved when combined with low-GWP fluid R1234yf. Overall, the integration of system flexibility with low-GWP refrigerants demonstrates a practical pathway toward a cleaner energy future and sustainable energy transitions, combining enhanced efficiency, cost-effectiveness, and climate benefits.
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Authors: , Nilay Shah, , Adam Hawkes
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Global renewable hydrogen trade is expected to play a key role in decarbonizing future energy systems. Yet hydrogen exporters may deviate from perfectly competitive behaviour to influence prices, similarly to the existing fossil fuel market, with important implications for consumer welfare and the pace of the energy transition. This study develops a global renewable hydrogen trade model that captures potential strategic interactions among exporters using a Stackelberg game-theoretic framework. The model is formulated as an Equilibrium Problem with Equilibrium Constraints (EPEC) and solved under three alternative equilibria: a profit-maximizing Nash equilibrium, a cost-minimizing Nash equilibrium, and a welfare-maximizing benchmark representing perfect competition. Results indicate that producers may strategically reduce their export quantities by up to 40 % relative to perfect competition to maximize profits. Such behaviour raises prices to a minimum of 4.5 USD/kg in 2050 across major import markets, thereby significantly eroding consumer surplus. Strategic behaviour of dominant exporters also shifts trade flows, reshaping the global allocation of hydrogen supply. Sensitivity analysis further reveals that financing costs play a key role in shaping strategic producers’ behaviour, with lower financing costs helping to reduce prices and stimulate demand. These findings highlight the implications of imperfect competition in global hydrogen trade and suggest that policy measures may be needed to mitigate potential negative consequences.
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Authors: , ,
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A multienergy framework can be used to manage and integrate various energy sources, contributing to the development of sustainable energy systems. In this study, the integration of hydrogen is investigated within microgrids as a multienergy framework, starting with a discussion and categorization of various types of microgrids. The production, transmission, storage, and utilization of hydrogen are examined, emphasizing components, such as electrolyzers and fuel cells, which connect hydrogen to the electricity network. Recognizing the significant role of hydrogen-based mobility in decarbonization, the infrastructure of hydrogen fueling stations is also explored. Through a detailed case study and the development of an operational model for multienergy microgrids incorporating hydrogen fueling stations, this chapter demonstrates that the storage can cover nearly 70% of the demand during peak hours. This enhancement is particularly effective in reducing load shedding during periods of low renewable energy availability and upstream grid failures.
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Authors:
Abstract: With the increasing use of renewable resources for various reasons such as environmental issues, energy security, sustainability, and energy storage, it is important to study the behavior of the power grid by considering these resources, especially in times of crisis. In situations where disasters cause heavy losses, using the capacity of these resources can help to continue energy supply and reduce losses. In this regard, this research examines and compares the process of restoring the power grid after a storm in the presence of renewable resources, including wind and solar power plants, and its uncertainty is considered through the information gap method. Modeling is implemented on the IEEE standard 69-bus network, and recovery is examined in two cases of the presence and absence of renewable resources. The results show that using renewable resources in the recovery process can reduce the time of the recovery process by 28% and the losses caused by disasters due to the storage and continuity of energy up to 34%, especially in inaccessible locations.
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Authors:
Abstract: To support decarbonization and enhance energy resilience, this study investigates how hydrogen technologies can be effectively integrated into renewable-based microgrids. For this purpose, a detailed operational planning model is proposed for a multi-energy microgrid that combines electricity and hydrogen distribution networks. This model captures network constraints, various technologies, and high-impact low-probability events, such as upstream grid outages. Four scenarios are investigated, including the renovation of gas-fired units to burn hydrogen, ELZs and fuel cells combinations, and reversible solid oxide cells, both with and without resilience planning. Results show that rSOC integration eliminates 100% of renewable curtailment, reduces load shedding by up to 85%, and achieves 1.5% lower annual operating costs compared to conventional hydrogen systems.
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