IET Smart Grid最新文献

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.

Nonintrusive load monitoring (NILM) load classification algorithms' performance and effectiveness are evaluated not only based on their classification accuracy but also based on their computational cost. In recent literature a lot of hybrid approaches have been proposed in order to enhance load classification accuracy. These methods simply combine different NILM algorithms to enhance load classification accuracy, without taking into consideration the increased computational costs. In other words, these methods improve load classification accuracy but at the expense of speed and efficiency, which make them impractical to use in real life. This paper provides a review and a categorisation of hybrid methods in NILM load classification and proposes a novel hybrid approach based on the instantaneous p-q load signature, which considers different characteristics of load signatures with the goal of computational cost reduction, which in itself can be classified as a new category of hybrid approaches.

The increasing penetration of distributed energy resources (DERs) and power electronic devices introduces dynamics notably different from those of synchronous machines. Their extremely high-order models demand tiny simulation time steps and substantial computational resources, posing significant challenges for power system simulation. Equivalent models, including surrogate models, reduced-order models (ROMs) and hybrid models, have garnered considerable attention. Although these equivalent modelling techniques provide powerful tools for power system simulation, their diversity and distinct characteristics necessitate a careful selection of appropriate models under different scenarios. This paper first summarises the challenges in DER-based power system simulation. Then, we categorise equivalence modelling techniques along with their advantages while addressing simulation difficulties and accordingly discuss the applicability of various models. Furthermore, unsolved challenges in power system simulation are identified and potential solutions are proposed. This review aims to facilitate the broader adoption of equivalent modelling techniques in power system simulation studies.

The varying topological configurations, generator commitments and dispatches, and dynamic load demand lead to changing system's strengths during the operations of networked microgrids. When the system's strengths significantly change, the fixed control gains at large devices may result in unsatisfactory system performance; this necessitates the tuning of the control gains at large devices to adapt to the changing system's strengths. In this paper, observer-based reinforcement learning (RL) is utilised to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing strengths of microgrids and networked microgrids. The RL agent in this framework augments an observer predicting system's strengths, from which the RL control policy will adjust accordingly to tune the PLL controller's gains towards the system's strengths. Also, to enhance the control performance, the recently introduced Barrier function-based RL framework is leveraged for the design of reward function to prevent the high frequency nadir. An operational 26 kV electric distribution system, which is modelled as networked microgrids, is used to illustrate the need and effectiveness of the proposed RL-tuned control.

Photovoltaic-storage-charging (PSC) systems, beyond delivering electric vehicle (EV) charging services, exhibit certain degrees of load flexibility, thereby enabling their participation in demand response (DR) markets. An optimal scheduling strategy for PSC systems that accounts for the coupled constraints between charging services and the DR market has been proposed in this paper. First, the inherent characteristics of system resources have been analysed, and mathematical models for both charging services and demand response have been developed. Then, a unified framework is constructed based on the concept of callable capacity, capturing the coupling relationship between the two service types. An optimisation model is formulated with the objective of maximising total system profit, while incorporating constraints such as market participation rules, baseline load definitions, and system operation limits. The callable capacities associated with different service mechanisms are treated as control variables to enable optimal dispatching of the system across both charging and demand response services. Finally, the effectiveness of the proposed strategy is validated through a case study of a real-world PSC system.

The battery is a critical component in electrochemical energy storage systems. High temperatures can accelerate battery degradation and create safety risks, such as thermal failure. Thus, effective heat dissipation is essential to reduce the operating temperature, extend battery life and minimise thermal failure risks. Maintaining a low-temperature differential among batteries also improves system efficiency and economic performance. This study proposes a cascaded DC-DC energy storage system that maintains battery temperature equilibrium based on module temperature trends and reduces temperature differences by distributing power across individual DC-DC converters. First, this study investigates the heat dissipation technology of energy storage batteries. Second, to enhance the speed of temperature homogenisation, a temperature trend forecasting the equalisation control method is introduced, building upon the average temperature equalisation and positive half-range temperature difference equalisation strategies. Finally, an experimental verification is conducted on a cascaded energy storage system consisting of eight DC-DC converters with a rated power of 11.6 kW. The results indicate that during charging, the temperature difference among battery cells remains below 2°C, whereas during discharging, the temperature difference is maintained below 1°C. Overall, temperature equalisation control based on cell temperature trend prediction demonstrates excellent performance in terms of both equalisation speed and effectiveness.

Faults in power transmission systems pose significant challenges due to the complexity and length of transmission lines. Effective fault detection, classification and location are essential for preventing further damage to the power grid. While travelling wave-based algorithms are commonly used for fault location, they often focus on identifying the fault's location without classifying the fault type. Accurate classification is crucial for enabling efficient and timely responses from protection systems. This paper introduces an integrated model for fault detection, classification and location using voltage signals from a single terminal of a series-compensated transmission line with a static synchronous series compensator (SSSC). The Gabor Transform (GT) is utilised for feature extraction, enabling both fault detection and classification. Travelling wave theory is then applied to identify the faulty segment and estimate the fault location. Additionally, a novel technique adaptively calculates the threshold value during the protection algorithm's execution. The proposed method is validated through a comprehensive analysis of various fault scenarios and sensitivity analysis. Numerical simulations in MATLAB/Simulink show that the model achieves 100% accuracy for fault detection, classification, and faulty segment identification, with 99.7925% accuracy for fault location estimation, demonstrating its effectiveness in fault management.