Energy Hub Research Articles

Providing frequency support to offshore energy hubs using hydrogen and wind reserves

A GIS-portal platform from the data perspective to energy hub digitalization solutions- A review and a case study

Multi-objective stochastic–adaptive robust optimization for energy management of grid-connected energy hubs including hydrogen and thermal storages, DGs and EVs, considering energy and reserve regulation market model

Iran, situated in the Middle East, is recognized as a prominent energy hub, with its economy heavily reliant on the exportation of energy. Iran currently faces significant water stress, underscoring the importance of examining its Water–Energy (WE) nexus. Hence, it is crucial to examine the Water–Energy (WE) nexus in this nation. This study evaluates Iran’s WE nexus from upstream to downstream in its energy subsystem (2007–2017) through an integrated framework combining water footprint analysis, water consumption methodologies, and nexus system modeling. This study assessed the WE nexus from upstream to downstream in Iran from 2007 to 2017. Key findings reveal that steam turbine power plants, particularly Ramin and Neka, exhibit the highest water consumption intensities, approximately 2.04 and 2.65 m3/MWh respectively, making them critical targets for efficiency improvements or retirement. Conversely, combined-cycle plants with dry cooling technology show significantly lower water intensity (0.18 m3/MWh), presenting viable alternatives. The study recommends shifting energy infrastructure towards combined-cycle and gas turbine plants to mitigate water stress, thus providing actionable insights for sustainable energy and water resource management in water-stressed regions.

This research proposes an advanced optimization framework for renewable energy hubs within integrated electrical and thermal networks, aimed at improving energy management. The motivation stems from the need for a more flexible and efficient solution that addresses the variability of renewable energy sources, such as wind and bio-waste units, while integrating storage solutions like hydrogen and thermal systems. The hypothesis is that combining a market-clearing price model with robust decision-making frameworks can optimize both economic viability and operational efficiency. The methodology adopts a two-tier optimization approach: the upper tier maximizes hub profits, and the lower tier minimizes operational costs through a market-clearing price model. The study also incorporates a robust optimization model that accounts for decision-dependent uncertainties, with a novel class of polyhedral uncertainty sets used for improved decision-making. Numerical results from case studies demonstrate that the proposed method increases the objective function by approximately 3%, and achieves a 25% faster solution time compared to the Benders decomposition approach. These findings support the conclusion that the proposed framework enhances both flexibility and economic performance of energy hubs, offering a viable solution for modern energy systems.

Considering rising environmental concerns and the energy transition towards sustainable energy, Singapore’s power sector stands at a crucial juncture. This study explores the integration of grid infrastructure with both generated and imported renewable energy (RE) sources as a strategic pathway for the city-state’s energy transition to reach net-zero carbon emissions by 2050. Employing a combination of simulation modeling and data analysis for energy trading and advanced energy management technologies, we examine the current and new grid infrastructure’s capacity to assimilate RE sources, particularly solar photovoltaic and energy storage systems. The findings reveal that with strategic upgrades and smart grid technologies; Singapore’s grid can efficiently manage the variability and intermittency of RE sources. This integration is pivotal in achieving a higher penetration of renewables, as well as contributing significantly to Singapore’s commitment to the Paris Agreement and sustainable development goals. While the Singapore’s power system has links to the Malay Peninsula, the planned ASEAN regional interconnection might alter the grid operation in Singapore and possibly make Singapore a new green energy hub. The study also highlights the key challenges and opportunities associated with cross-border energy trade with ASEAN countries, including the need for harmonized regulatory frameworks and incentives to foster public–private partnerships. The insights from this study could guide policymakers, industry stakeholders, and researchers, offering a roadmap for a sustainable energy transition in Singapore towards meeting its 2050 carbon emission goals.

Reimagining landfills as energy hubs: A new integrated approach for biomethane and green hydrogen production

Risk-averse resilient joint expansion planning of integrated energy distribution systems with energy hubs

Abstract Combustion of fossil fuel by single-cycle gas turbines in offshore oil and gas (O&G) facilities on the Norwegian continental shelf contribute significantly to Norway’s total greenhouse gas emissions. The CleanOFF Hub Research Project aims to reduce those emissions through innovative solutions, such as large-scale offshore hubs delivering low-carbon power to O&G platforms. Within this context, this paper presents an advanced electromagnetic transient (EMT) model for a hydrogen-based energy hub connected to a gigawatt-scale wind farm, designed to power six isolated O&G platforms. Building on previous research, this paper introduces an EMT model that addresses earlier limitations in simulating fast electrical transients and incorporates a more sophisticated virtual synchronous machine (VSM) architecture enabling the study of critical millisecond scale transient phenomena. Key contributions include the development of a publicly available EMT model of a gigawatt-scale wind-powered system with primary and inertial frequency control reserves provided by four VSMs, and the integration of secondary reserves from large grid-following electrolyser and fuel cell systems. These advancements facilitate robust analyses of dynamic behaviors and stability challenges in large-scale, isolated offshore power systems, contributing to the goal of decarbonizing the Norwegian continental shelf.

Future-powered transportation fleets and mobility in eco-friendly cities: Economic scheduling interactions for integrated energy hubs via micromobility electric vehicles

Machine learning-based economic model predictive control for energy hubs with variable energy efficiencies

Energy hubs integrating renewable energy sources and demand response programs for cost-effective operations

Strengthening energy hub resilience: KNN-based cyber protection with demand-side data modification for hydrogen, electrical, and thermal storage

Smart Energy Hub Frequency Control-Based Machine Learning

Bi-level operation model for energy hub based on energy-carbon coordination optimization framework

Towards the evolution of decarbonized mobility for sustainable city futures: synergistic interactions in urban energy hubs via electric public transportation fleets and carbon capture

Medium-term optimal operation of energy hub considering risk assessment and uncertainties of energy price and renewable energy resources

Fueling the future: Optimizing Power-to-X production in renewable energy hubs through flexible operating units

Consensus-based optimal operation of multi-agent renewable energy hubs considering various graph topologies

Abstract Offshore wind power can greatly reduce local CO 2 emissions from offshore oil and gas installations, but variability must be balanced through energy storage and other generation capacity. This study investigates different energy supply concepts and analyses their feasibility and performance through simulations based on a receding horizon optimisation approach. This approach allows simulations that resemble real-time operation with plans being updated as better information becomes available.