IET Smart Grid最新文献

The current system frequency response (SFR) model that incorporates the wind farm fails to fully account for the operational variations of wind turbine generators (WTGs) across varying wind speeds and different frequency regulation control modes, indicating potential for improvement. To address this issue, this paper first considers the virtual inertia, droop, overspeed de-loading and pitch angle de-loading control of the WTG to establish a frequency response model of the WTG. Second, based on the different operating states of WTGs, the equivalent frequency response model of the wind farm is aggregated through unit grouping, and an extended SFR model is constructed by further integrating it with the traditional SFR model. Then, a detailed simulation model of the wind farm is established on the DIgSILENT PowerFactory simulation platform to validate the proposed extended SFR model. The results show that the proposed model can accurately track the frequency response characteristics of the detailed model. Finally, using the proposed SFR model, an analysis is conducted on the impact of the wind farm's frequency regulation parameters and wind speed scenarios on system frequency stability.

The rapid expansion of cloud computing has intensified the environmental impact of large-scale data centres, which now represent a significant portion of global electricity consumption. Traditional scheduling strategies typically optimise performance or cost, disregarding the fluctuating carbon intensity of regional power grids. This study proposes a dynamic carbon-aware scheduling framework that integrates real-time carbon intensity forecasting with multi-objective optimisation and adaptive rolling-horizon control. The proposed model simultaneously minimises operational cost and greenhouse gas emissions by intelligently shifting computational workloads across time and geography in response to renewable energy availability. The framework combines an ensemble forecasting module, using long short-term memory (LSTM) and gradient boosting regression, with a mixed-integer linear programming (MILP) model solved via the $��\epsilon �$-constraint method. It adaptively updates scheduling decisions based on updated carbon forecasts and workload arrivals. Experimental validation on real datasets from the UK National Grid and Google Cloud workload traces demonstrates an average $��25�\%�$ reduction in $��{\text{CO}}_2�$ emissions, a $��7�-�10�\%�$ improvement in cost efficiency and less than $��3�\%�$ performance degradation compared to conventional schedulers. Pareto front analysis further reveals actionable trade-offs between economic efficiency and environmental sustainability. The results confirm that integrating operational research with carbon intelligence enables cloud infrastructures to become both cost-effective and climate-aligned.

This paper presents a novel bi-level optimisation framework to coordinate the operations of transmission system operators (TSOs) and distribution system operators (DSOs) under wind power uncertainty. At the upper level, the TSO minimises system-wide generation costs while managing wind forecast errors through chance constraints. At the lower level, the DSO responds to locational marginal price and nodal carbon intensity signals issued by the TSO and minimises total operational costs, including electricity procurement, local generation and carbon costs. By incorporating the carbon emission flow theory, the framework accurately quantifies carbon intensity at each transmission node, providing essential signals that enable the DSO to jointly optimise economic performance and carbon reduction. To solve the complicated bi-level optimisation problem, we reformulate it as a single-level mixed-integer programme to ensure computational tractability. Numerical simulations demonstrate that the proposed framework effectively enhances system-wide economic efficiency and reduces carbon emissions. The results further reveal that the selected confidence level under uncertainty has a pronounced impact on the optimal economic dispatch outcomes.

The rapid expansion of electric vehicles (EVs) in Thailand necessitates a strategic approach to developing an efficient EV charging station (EVCS) infrastructure. This research study introduces dual-strategy algorithms for EVCS deployment, aligning with the national 30@30 policy target to promote sustainable transportation. Monte Carlo simulations are employed to estimate EV charging demand, accounting for user behaviour, public charging needs, EV fleet data, and various charging scenarios. Simulated demand profiles and utilisation factors are used to determine the required number of chargers for different EV types. Charging station locations along nationwide highways and within provinces are optimised using the maximum coverage and Dijkstra's shortest path algorithms. These approaches leverage geographic information system (GIS) data from OpenStreetMap, which allows realistic representations of road networks, travel distances, and route complexities. The strategy prioritises traffic density and incorporates constraints such as demand, coverage radius, existing infrastructure and network traffic patterns. Charger distribution for highways and urban areas is optimised using the knapsack problem framework to minimise waiting times and improve sharing efficiency. This methodology can be updated annually using new EV growth projections and infrastructure needs. Case studies include long-term planning for EVCS deployment on inter-provincial highways and in metropolitan areas, demonstrating its effectiveness in achieving the country's ambitious sustainable transportation targets.

With the advancement of port electrification, the stable operation of port-ship systems face significant challenges due to uncertainties in renewable energy output and ship throughput. To address these issues, this paper proposes a robust operational optimisation method for port-ship systems that considers both throughput uncertainty and ship-participated grid-forming support. First, a system model coupling logistics and energy flows is established for sequential scenarios involving ship arrival, cargo handling, cargo transportation and cargo storage. Second, a polyhedral uncertainty set is constructed to represent ship throughput uncertainty and is incorporated into the optimisation model. Third, the bidirectional charging capability of all-electric ships (AES) is leveraged to provide grid-forming support for high-power impact loads, such as gantry crane start-ups and uncertain operating conditions. Finally, a column-and-constraint generation algorithm is applied to minimise the operational costs of the port-ship system. Simulation results demonstrate that the proposed method improves both the economic efficiency and the robustness of the system.

Distributed renewable power generation technologies have pushed a clean energy transition from fossil fuels to sustainable sources worldwide. Meanwhile, the advancement of energy conversion technologies has driven the concept of integrated energy systems that focus on the integration of multiple energy types in a specified region. With both energy production and consumption abilities of energy prosumers, peer-to-peer energy sharing has become increasingly important to enhance energy utilisation and efficiency. Motivated by the recent extension of energy sharing across multiple energy types, a systematic review of key framework, technologies and implications pertinent to IES energy sharing is undertaken. By putting emphasis on the newest research in multi-energy sharing, a broader vision of the roles and impacts of prosumers in future multi-energy systems is provided as a reference to energy sharing related research and development. Furthermore, the physical modelling frameworks of multi-energy sharing are summarised, and the discussion needs to be further extended to research on multi-energy sharing as well as the investigation of energy sharing implications for other energy services. The prospects and challenges of energy sharing in IES are also discussed to envisage potential future research directions.

This paper proposes a day-ahead multi-microgrids (MMG) data-driven robust scheduling model incorporating the cap-and-trade CO₂ emission trading system (ETS), peer-to-peer (P2P) energy trading based on Nash bargaining Theory and demand response (DR). The adaptive robust optimisation (ARO) technique is applied to handle the uncertainties of photovoltaic (PV), electric vehicle (EV) and normal loads with the utilisation of battery energy storage system (BESS) as an adaptive recourse control. The boundaries of uncertain variables are constructed based on the data-driven risk-adjusted method with Wasserstein distance technique to enhance accuracy and reliability of the uncertainty sets when the true knowledge of probability distributions does not exist. The proposed approach is validated through extensive simulations on both individual and interconnected three-MMG systems. Results confirm that the peer-to-peer (P2P) scheme, employing a Nash bargaining solution, ensures an equitable distribution of economic benefits among all participants. Furthermore, the integrated CO₂ ETS effectively reduces both operational costs and emissions under a carbon-regulated environment. The energy exchanges between customers are also demonstrated to further contribute to the overall reduction of system emissions. The study further investigates how varying forecasting accuracies for uncertainty variables influence the construction of uncertainty boundaries and the ensuing cost and emission outcomes. A comparative analysis against deterministic model and traditional two-stage robust optimisation (RO) model with predefined boundaries based on the available probability distribution demonstrates the superior reliability of the proposed technique. The constructed data-driven uncertainty sets could potentially provide a probabilistic guarantee, based on user's preferred confidence level, that future realisations of forecast error will lie within them. This ensures that the predetermined recourse actions will remain feasible and maintain system security.

Power networks are increasingly becoming complex and multidimensional, with a high level of interdependency with other infrastructures, which requires different approaches for modelling, planning and analysis. One critical infrastructure that is increasingly integrated with the physical power system is the cyber infrastructure, which helps improve the observability, security and control of a network. However, reliability during normal conditions and the resilience of the system during extreme events can both be affected, as the threats to the system can be of a physical or cyber nature. Hence, utilities must rethink how to optimally choose investments in the existing infrastructure to enhance both reliability and resiliency. This study proposes a robust model for interdependent cyber and physical networks in power systems that optimises the investment cost to improve the reliability and resilience of the system, considering the cost of energy not supplied. Different models are proposed to realistically reflect the cyber–physical power system, such as extreme weather events and cyber–physical threat models. The optimisation was modelled as a mixed-integer nonlinear optimisation problem, and a heuristic optimisation method was used to demonstrate the effectiveness of the model.

This article presents a comprehensive taxonomy and survey of Cybersecurity Control Schemes (CCS) tailored for Smart Grids (SG), with a particular focus on vulnerabilities in the IEC 61850 standard and the countermeasures provided by IEC 62351. The taxonomy introduces four main dimensions to analyse 25 tailored CCS for SG, covering research from 2013 to 2023. It establishes classification criteria that encompass: (i) deployment strategies, categorised into five levels based on SG supervision hierarchy; (ii) security mechanisms, including prevention, detection and response mechanisms; (iii) mitigated threats, such as False Data Injection (FDI), Denial-of-Service (DoS), masquerade attacks, replay attacks, data tampering; and (iv) protected applications within the power system domain, such as supervisory, protection, and control applications, time synchronisation and synchrophasor data applications. The survey evaluates CCS characteristics aligned with IEC 61850 and IEC 62351 standards and provides a structured analysis of CCS deployment, commonly used data parameters, security mechanisms and response actions for mitigating cyberthreats in SG. Finally, by integrating lessons from industry standards, academic research and practical considerations, this study identifies open challenges and outlines future research opportunities to enhance CCS robustness. The findings offer actionable insights for researchers and practitioners seeking to strengthen the cybersecurity of SG systems.

By focussing on the limitations of fixed dead time (DT) in conventional reclosing methods, this paper highlights its significant adverse impact on transient stability of distributed energy resources (DERs) in microgrid. This vulnerability is particularly pronounced in systems with low inertia, arising from renewable energy-based DERs (REBDERs) that inherently lack inertia or exhibit limited inertial response even when connected via synchronverters, as well as from small-scale synchronous generator-based DERs (SGBDERs). Accordingly, an improved adaptive auto-reclosing scheme is proposed to effectively address this challenging task. The proposed scheme aims to reconnect the system in the shortest possible time while maintaining the transient stability of the microgrid during permanent faults. To this end, an adaptive DT is determined based on potential energy assessment, ensuring that the potential energy associated with the DER is minimised. To address this, two indices based on the first- and second-order time derivatives of potential energy are introduced. A polynomial least squares error (LSE) method is then employed to extract the temporal trends of these indices and predict future behaviour using a minimal number of sampling instance. Hence, the proposed algorithm can be an effective solution for microgrids with low inertia generators that are prone to rapid instability.