The increasing integration of renewable energy resources in decentralised energy systems (DESs), particularly in weak-grid or isolated environments, has intensified operational uncertainty due to weather-driven variability. This variability often leads to renewable curtailment, insufficient system flexibility, and reliability concerns during extreme climatic events. Consequently, there is a growing need for forecasting and control approaches that maintain accuracy under changing climatic conditions while remaining reliable when available data are limited. Conventional physics-based models often struggle to capture the nonlinear and time-varying dynamics of DESs, whereas purely data-driven methods tend to lose predictive capability when exposed to unfamiliar climatic regimes. This study aims to improve multi-energy forecasting and operational control in DESs by developing a hybrid modelling framework that integrates Physics-Informed Machine Learning (PIML) with Climate-Aware Digital Twins (CADTs). The key novelty lies in enabling the digital twin (DT) architecture to dynamically adapt its internal state estimation and forecasting behaviour using climate-dependent inputs, while simultaneously enforcing thermodynamic and system-level physical constraints. The proposed CADT-PIML framework employs XGBoost models for photovoltaic and wind power forecasting, multilayer perceptrons to capture energy storage dynamics, and long short-term memory networks to predict electricity price fluctuations. Physical consistency is ensured through embedded thermodynamic constraints that guide the learning process and maintain interpretability. The proposed approach is validated through a rural microgrid case study to assess forecasting accuracy, operational reliability, and renewable energy utilisation. Results demonstrate that the CADT-PIML framework improves forecasting accuracy by 27.2%, reduces unmet load by 38.4%, and decreases renewable curtailment by 21.9% compared with conventional DT approaches. These findings indicate that climate-aware, physics-guided DTs can provide a robust and adaptive solution for intelligent decentralised energy management under increasingly variable climatic conditions.

Achieving Great Britain’s 2050 net-zero target requires strategic integration of hydrogen into the national energy system. This study evaluates the system-wide impacts of hydrogen blending (0–100%) using a bi-level optimisation framework that combines long-term cooperative investment planning with short-term operational Optimal Power and Gas Flow (OPGF) simulation. The strategic layer models infrastructure investment decisions under a cooperative game-theoretic structure, where system value is allocated among electricity, hydrogen production, and storage technologies using the Shapley-value payoff mechanism. Contrary to traditional centralised cost-minimisation models, our findings demonstrate that a cooperative planning structure identifies superior transition pathways. Comparative results reveal that at 100% hydrogen penetration, the cooperative framework reduces total system CO2 emissions by 31%, lowers operational costs by 26%, and decreases total electricity supply requirements by 8% relative to centralised planning. Furthermore, the cooperative approach significantly enhances economic resilience, yielding a more robust Net Present Value (NPV) across all blending levels compared to centralised planning, while ensuring project profitability at lower blending thresholds (20%) where traditional models remain loss-making. Simulation results indicate that hydrogen blending up to 20% maintains operational stability with manageable increases in operational cost. Full hydrogen conversion (100%) increases peak electricity supply requirements by approximately 30% relative to low-blending scenarios due to electrolysis-driven load expansion and conversion losses. The findings demonstrate that hydrogen blending represents a viable transitional pathway when supported by integrated infrastructure development and cooperative stakeholder coordination, enabling a more efficient and economically sustainable phased progression towards Great Britain’s 2050 net-zero target.

This paper presents a real-time Digital Twin (DT) framework for power distribution networks that leverages Electric Vehicles (EVs) as flexible energy storage to support peak load management. EVs are coordinated through aggregators, serving as an interface between the network operator, energy market, and EV owners. The DT-physical system communication remains vulnerable to cyberattacks and data manipulation. To address this, an Artificial Neural Network (ANN) is trained on historical data to detect and mitigate such attacks, ensuring robust operation. The framework is applied to Jordan's distribution network with high renewable penetration, demonstrating its ability to deliver flexibility services, detect cyberattacks, and accurately reconstruct corrupted measurements, achieving a maximum Mean Squared Error (MSE) of 3.46 & times; 10-7.

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments.

Achieving Great Britain’s 2050 Net Zero target requires hydrogen policy that can remain effective under uncertain energy market conditions. This paper proposes a policy-navigation framework that combines a bi-level game-theoretic investment model based on Shapley value allocation with an integrated optimal power–gas-flow (OPGF) model to evaluate CO2 pricing and Contracts for Difference (CfD) mechanisms. We apply cooperative game theory to model the technical coupling among the 18 technology players. By using the Shapley value to allocate the resulting joint cost savings fairly, we can design coordinated policies (CfD and Carbon Pricing) that balance the macro-level trade-offs between investor returns (generators), public spending (government), and energy bills (consumers). Results show that while high CO2 prices effectively drive decarbonisation, they do not provide sufficient investment stability for hydrogen in low-price markets, highlighting the need for complementary CfD support. Using the Marginal Abatement Cost of Hydrogen (MACH) as a central evaluation metric, we identify an actionable policy corridor with CO2 prices of £200–300/tCO2 and CfD support of £100–200/MWh, balancing cost-effective decarbonisation with investment viability and fiscal performance. Additionally, the Interactive Policy Navigator (IPN) enables efficient exploration of 288 policy scenarios, translating high-dimensional quantitative results into accessible and actionable policy insights for policymakers.

The pathway towards net-zero emissions requires a clearly defined strategy that effectively integrates renewable energy, enhances system flexibility, and ensures economic viability. This paper develops a game-theoretic framework for integrated energy system planning in Great Britain (GB), aimed at addressing scalability and cross-sector policy interactions. The proposed framework captures competition among electricity, gas, and hydrogen systems within a multi-vector context. This framework is based on a bi-level optimisation structure that links long-term capacity investment decisions with short-term operational strategies, enabling the planning of integrated system dynamics under realistic constraints. Key contributions include the evaluation of hydrogen-based vector-coupling storage (VCS) as a flexible solution for enhancing system reliability and cost-effectiveness. Through this framework, the study explores how renewables, hydrogen systems, and infrastructure can synergise to support the United Kingdom’s energy transition goals. The findings provide actionable insights for policymakers and industry stakeholders, offering tools to guide investment, infrastructure requirements, and decarbonisation efforts.

The increasing exposure of modern power systems to climate-induced hazards, cyber threats, and operational uncertainty has intensified the energy trilemma of sustainability, security, and affordability. Rapid decentralisation and digitalisation driven by renewable integration, microgrids, and active demand participation have rendered conventional planning and operational approaches inadequate for ensuring resilient and sustainable electricity networks. This challenge requires adaptive, data-driven frameworks that provide real-time situational awareness, predictive intelligence, and coordinated control under uncertainty. This paper presents a PRISMA-guided systematic review of Digital Twin (DT) technologies, with a particular focus on Climate-Aware Digital Twins (CADTs) and AI-driven analytics for enhancing sustainability and resilience in modern power systems. The review synthesises recent advances in predictive forecasting, decentralised energy management, grid resilience enhancement, and hazard-informed system restoration. Key application domains include microgrid resilience, demand response optimisation, renewable-dominated networks, and satellite-assisted recovery following extreme events. Enabling technologies such as machine learning, edge-cloud computing, blockchain, and the Internet of Things (IoT) are examined as foundational components supporting real-time synchronisation, secure data exchange, and autonomous control. Across the reviewed literature, four persistent challenges are identified: data latency and availability constraints, high computational and modelling complexity, limited interoperability with legacy infrastructure, and unresolved cybersecurity risks. Building on these findings, the paper proposes a strategic roadmap for integrating DTs with climate-aware forecasting and adaptive control architectures, highlighting pathways towards intelligent, self-healing, and hazard-resilient power systems.

The digitalisation of power systems introduces substantial vulnerabilities, particularly concerning system stability and security, due to the growing risk of malicious cyber attacks. These threats, often enabled by the proliferation of Internet of Things assets, can target critical infrastructure by manipulating system load or altering renewable power generation, both of which directly affect the system security margin. This paper proposes a novel cyber-resilient optimisation strategy (CROS) framework to enhance cyber-resilience in power systems, particularly under conditions of high renewable power penetration. The framework explicitly accounts for the interplay between cyber and physical components and addresses how adversarial interventions can impact load and renewable generation inputs. Central to this approach is a cyber-conscious strategy (CCS) which intends to increase the cyber-resilience radius in the face of unknown cyber attacks without any information about the intention and characteristics of attacks. By embedding the cyber-flexible security service (CFSS), the framework supports real-time corrective actions, allowing operators to adapt flexibly to evolving cyber-threat scenarios. The CFSS defines a dynamic security margin, reflecting the system's ability to maintain system security under unknown cyber threats. The proposed multi-stage, bi-level co-optimisation model integrates a cost-risk analysis to guide techno-economic decision-making, empowering operators with a proactive and adaptive operational strategy to mitigate the impact of cyber incidents on modern, renewables-rich power systems.

Peer-to-peer (P2P) energy trading offers a decentralised framework for integrating distributed renewable resources. When local renewable energy generation exceeds demand, surplus electricity can be converted into hydrogen for long-duration storage, providing flexibility beyond the electricity vector. However, most existing P2P markets are focused only on electricity, do not account for network losses and are not designed to coordinate multi-vector trading with inter-temporal couplings. To address these gaps, we propose a distance-aware periodic double auction (DA-PDA) market-clearing mechanism that extends the conventional PDA by incorporating loss-aware pricing and enabling trades between peers with the lowest loss cost. The DA-PDA provides a distributed, market-based coordination mechanism for joint electricity-hydrogen trading, improving efficiency through dynamic price signals. The framework enhances system-level performance by reducing renewable curtailment, increasing utilisation of surplus electricity and enabling hydrogen-supported flexibility. Using a real-world case study, we demonstrate that sector-coupled P2P markets can improve local social welfare and act as an effective energy-conservation mechanism in highly renewable, electrified systems.

The slow implementation of green hydrogen as an alternative to blue or grey hydrogen is constrained by technical and economic feasibility. Therefore, this study presents a technoeconomic model for green hydrogen production in the Ma’anAqaba corridor in Jordan. The research provides a novel assessment of the corridor using a high-resolution dispatch simulation that covers 8760 hours of output from a hybrid wind-solar system. The model addresses the water-energy nexus by considering a seawater transport system of 300 km. The study also used Jordan’s Investment Environment Law (2022) and Gas Law (2025) to determine the study’s economic parameters. The technical results of the model showed that the hybrid wind-solar system in the corridor can achieve a capacity factor of $\mathbf{4 5 . 2}$ percent. At the same time, the economic results presented a levelized cost of hydrogen of 4.20 USD per kg in 2026, projected to decrease to 1.40 USD per kg by 2050 under specific constraints. However, to maintain an internal rate of return of 18.2 percent to 14.1 percent, a 5 percent preferential tax rate is required to enhance bankability.

The integration of electricity, heating, cooling, and hydrogen systems in multi-energy microgrids (MEMGs) enhances operational flexibility but introduces significant uncertainty, strong system coupling, and degradation-related challenges. This paper proposes a Learning-Enhanced Stochastic Model Predictive Control (LE-SMPC) framework that integrates trustworthy machine learning forecasting with constraint-aware optimisation. The proposed approach combines (i) degradation-aware modelling of fuel cells and batteries, (ii) permutation-based feature attribution embedded within the learning process to ensure interpretability, and (iii) stochastic MPC with chance constraints for robust decision-making under uncertainty. In this work, resilience is referred to as the ability to maintain safe and feasible operation under stochastic variability and progressive component degrada tion through adaptive control. Simulation results demonstrate that the proposed method achieves approximately 40% reduction in forecasting error (based on RMSE comparisons with baseline models), while consistently main taining constraint satisfaction under representative degradation conditions, including up to 18% fuel cell voltage drop and 30% battery capacity loss, as well as varying demand levels. These results highlight that the proposed LE-SMPC framework improves prediction reliability, enhances control robustness, and provides transparent, operator-oriented decision support for multi-energy system management.

Achieving Great Britain's 2050 net-zero target requires coordinated integration of electricity, gas, and hydrogen systems. This paper presents a game-theoretic optimisation framework that evaluates competitive and cooperative investment and operational strategies within a bi-level structure combining long-term planning and short-term operational constraints. The competitive scenario is modelled through a Nash-Cournot equilibrium, while the cooperative scenario applies the Shapley value to ensure a fair allocation of costs and benefits among technologies. Results show that both approaches enable decarbonisation, but cooperation delivers superior economic efficiency at the 2050 peak demand, achieving a 57% reduction in operational costs and complete decarbonisation, compared to residual emissions of 8161 tonnes under competition. Competitive strategies favour flexibility technologies such as Power-to-Gas (P2G) (11.7%) and Battery Energy Storage (BESS) (11.4%), whereas cooperative planning utilises lower flexibility (P2G 3.4%, BESS 4.5%) and greater nuclear baseload (20%-26%). Shapley value analysis quantifies each technology's marginal contribution, identifying hydrogen technologies as major value drivers, while gas-to-hydrogen reforming with carbon capture and storage (G2G-CCS), biomass, and combined heat and power (CHP) require policy support. When market conditions are not favourable, electricity technologies require between 0.82 pound and 2.16 million in financial support. The paper findings offer quantitative insights to guide policy development that incentivises collaboration and coordinated planning, supporting a resilient, fair, and economically efficient pathway to a net-zero energy system for Great Britain.

Decarbonising domestic heating remains a major challenge, particularly in gas-dependent and fuel-poor regions. This paper investigates hydrogen-based and alternative decarbonisation pathways for residential heating in the North of Tyne (NoT) region, UK. A multi-system perspective (MSP) framework—combining qualitative scenario analysis with quantitative energy system modelling—evaluates the effects of socio-technical interventions (STIs), technology adoption (heat pumps and hydrogen boilers), and hydrogen blending on energy demand, CO2 emissions, and system costs to 2050. Monte Carlo simulations capture behavioural uncertainties, while a game-theoretic investment model supports long-term planning. Results show that while STIs significantly reduce demand, they cannot alone achieve net zero. Hydrogen blending offers limited short-term benefits, whereas heat pumps deliver the lowest operational costs and emissions, representing the most efficient pathway under full electrification. Hydrogen boilers, though cost-intensive and less efficient operationally, show high long-term net present value (NPV £318–624 million), suggesting potential economic payoff over time. By 2050, with full CCS deployment and high renewable penetration, operational emissions are nearly eliminated across all scenarios. The findings highlight the importance of integrated planning, investment coordination, and social engagement to deliver a resilient and cost-effective low-carbon heating transition.

This study examines hydrogen-based and alternative strategies for decarbonising residential heating in the North of Tyne (NoT) region, UK, focusing on energy efficiency and conservation. A multi-system-perspective framework integrating scenario analysis and quantitative energy-system modelling is applied to assess socio-technical interventions, technology pathways (heat pumps and hydrogen boilers), and hydrogen-blending levels up to 2050. Monte Carlo simulations and a game-theoretic investment model are used to evaluate energy demand, CO2 emissions, and system costs. The results show that socio-technical interventions substantially reduce energy demand but are insufficient alone to reach net zero. Hydrogen blending provides modest emission reductions, while full electrification via heat pumps is most cost-effective in the long term, particularly with carbon capture and storage (CCS). A hybrid 50/50 heat pump–hydrogen-boiler pathway with CCS post-2040 presents a practical transition option. The findings highlight the importance of coordinated infrastructure planning and societal engagement for achieving deep heating decarbonisation.

The transition to decentralised, renewable driven power systems introduces new challenges for grid affordability and resilience. This paper presents Climate Adaptive Grid Intelligence (ClimaGrid), an AI -assisted, multi-layer control framework designed to optimise distributed energy resource (DER) dispatch under dynamic environmental and market conditions. ClimaGrid integrates three coordinated mechanisms: Adaptive Priority Aware Dispatch (APAD) for load profile smoothing, weather-informed resilience control for hazard response, and market-responsive energy allocation to minimise operational cost. Using a real -world dataset comprising load demand, PV and wind generation, and spot market prices, we demonstrate that ClimaGrid reduces peak load by 29.2% and peak -to-average ratio by 17.6%. In resilience simulations, it achieves full recovery within 12 minutes of a weather-induced generation drop, over three times faster than conventional rule -based strategies. Furthermore, ClimaGrid lowers grid import costs by 27.4% during price spikes, while maintaining DER coverage above 60%. These results validate ClimaGrid as a robust and economically efficient solution for future smart grid operation.

Data centres (DCs) are critical yet energy-intensive infrastructures, with rising operational costs and Carbon emissions due to their continued reliance on fossil-based grid. This paper presents a Mixed Integer Linear Programming (MILP) optimisation framework for decarbonising standalone DCs through coordinated scheduling of PV, wind, Battery Energy Storage System (BESS), diesel, and grid supply. The model incorporates demand-side management, stochastic renewable variability via Monte Carlo simulations, and techno-economic constraints. A case study shows up to 62.7% CO2 reduction and 82% cost savings compared to baseline operation powered exclusively by grid imports. Load shifting emerged as the most cost-effective strategy, while diesel-constrained scenarios provided greater emissions reduction. The framework supports real-time visualisation, scenario evaluation, and future integration of spatial load shifting and thermal storage systems.

Hydrogen could be generated, stored, transported, and consumed in various ways, making it a promising solution to carbon emission reduction. However, key questions still remain in how hydrogen could be appropriately integrated into energy systems over time while coupling with different sectors. This has led to model-based studies of the whole system value of hydrogen in future energy systems, and the near-term actions and long-term strategies required to facilitate the transition to low-carbon energy systems with hydrogen. In this paper, a systematic review of the existing model-based studies in this area was conducted. A summary of hydrogen applications in energy systems was made, with statistics of publications and projects revealing the fast-growing interest in hydrogen in the past several years. The modelling methods used to investigate the system integration of hydrogen was summarised from over 130 publications. This paper also identified the gaps in modelling capability and potential future research topics: 1) balance between the resolution and modelling complexity, 2) inclusion of all uncertain factors of hydrogen pathways, 3) advancement of modelling approaches to address the chicken-and-egg dilemma of hydrogen economy development, and 4) a more detailed and comprehensive coverage of various interactions between hydrogen and other sectors.

Renewable energy sources (RESs) are increasingly being recognized as sustainable and accessible alternatives for the energy future. However, their intermittent nature poses significant challenges to system reliability and stability, necessitating the integration of energy storage systems (ESSs) to ensure sustainability and dependability. This study examines various ESS alternatives, evaluating their suitability for different applications using a multi-criteria decision-making (MCDM) approach. The methodology accommodates diverse criteria types, including qualitative and quantitative factors, represented as linguistic terms, interval values, and crisp numerical data. A techno-socio-economic framework for ESS selection is proposed and applied to Jordan’s unique energy landscape. This framework integrates technical performance, economic feasibility, and social considerations to identify suitable ESS solutions aligned with the country’s renewable energy goals. The study ranks twelve energy storage systems (ESSs) based on key performance criteria. Pumped hydro storage (PHS), thermal energy storage (TES), supercapacitors (SCs), and lithium-ion batteries (Li-ion BESS) lead the ranking. These systems showed the best performance in terms of scalability, efficiency, and integration with grid-scale applications in Jordan. Key applications analyzed include renewable energy integration, grid stability, load shifting, peak load regulation, frequency regulation, and seasonal energy storage. Results indicate that Li-ion batteries are most suitable for renewable energy integration, while flywheels excel in grid stability and frequency regulation. PHS was found to be the preferred solution for load shifting, peak load regulation, and seasonal storage, with hydrogen storage emerging as a promising option for long-duration needs. These findings provide critical insights to guide policy and infrastructure planning, offering a robust model for comprehensive ESS assessment in energy transition planning for countries facing similar challenges.

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with an Attentive Long Short-Term Memory (LSTM) network to enhance forecasting performance in renewable-integrated smart grids. The CNN component captures spatial dependencies from multivariate inputs, comprising meteorological variables and generation data, while the LSTM module models temporal correlations in historical load patterns. An embedded attention mechanism dynamically weights input sequences, enabling the model to prioritise the most influential time steps, thereby improving its interpretability and robustness during demand fluctuations. ST-CALNet was trained and evaluated using real-world datasets that include electricity consumption, solar photovoltaic (PV) output, and wind generation. Experimental evaluation demonstrated that the model achieved a mean absolute error (MAE) of 0.0494, root mean squared error (RMSE) of 0.0832, and a coefficient of determination (R2) of 0.4376 for electricity demand forecasting. For PV and wind generation, the model attained MAE values of 0.0134 and 0.0141, respectively. Comparative analysis against baseline models confirmed ST-CALNet’s superior predictive accuracy, particularly in minimising absolute and percentage-based errors. Temporal and regime-based error analysis validated the model’s resilience under high-variability conditions such as peak load periods, while visualisation of attention scores offered insights into the model’s temporal focus. These findings underscore the potential of ST-CALNet for deployment in intelligent energy systems, supporting more adaptive, transparent, and dependable forecasting within smart grid infrastructures.