Adib Allahham

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.

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. 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.

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.

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.

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. Simulation results indicate that hydrogen blending up to 20% maintains operational stability and positive economic performance, with manageable increases in operational cost. Emissions reductions are realised when blending is combined with carbon capture and storage (CCS) deployment or through higher CO₂ pricing. 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. Sensitivity analysis shows that carbon pricing significantly reduces system emissions while moderately increasing hydrogen marginal costs and affecting Net Present Value. Under coordinated infrastructure planning, Net Present Value increases with hydrogen penetration, with full hydrogen deployment delivering the highest long-term system value. The findings demonstrate that hydrogen blending represents a viable transitional pathway when supported by integrated infrastructure development, CCS deployment, and appropriate carbon pricing, enabling a phased progression towards Great Britain’s 2050 net-zero target.

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.

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.

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.