Search Results (80)

The challenges of ensuring the security of electricity supply (SoES) in large aluminium smelters—particularly those that are self-supplied—provide a compelling rationale for further investigation, as research on this class of industrial systems is limited. Firstly, this paper presents an expert technical perspective on the distinct characteristics and operational challenges associated with aluminium potline loads and their supply systems in self-supplied aluminium smelters. This study then examines the supply infrastructure at Emirates Global Aluminium’s plant in Dubai, which has an installed power generation capacity of 3000 MW, supplying a 2000 MW load on a continuous basis through a network of three 132 kV substations. This high-voltage network is modelled and simulated using the CYME network analysis software module. We consider the following key approaches to ensure stable system voltage for desirable SoES: steady-state voltage control, outage planning and reactive power reserve management, active power flow management and load participation. We then study the influence each of these has on the system voltage and, hence, on the overall SoES of the smelter, using time-domain voltage and frequency curves at key network nodes and active power flow through important network interconnectors. The simulation results clearly demonstrate a significant improvement in the base case event by positively damping the oscillations in these responses, highlighting the significance of maintaining a healthy system voltage within a limit of ±2% of the nominal voltage to ensure SoES of the smelter. Full article

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems. Full article

South Korea’s 11th Long-term Plan for Transmission and Substation Equipment (LPTSE, 2024–2038) invests KRW 72.8 trillion (USD 52.3 billion) to integrate 91.9 GW renewables while securing supply for semiconductor/artificial intelligence demand concentrated in the Seoul Metropolitan Area. This study aims to quantify LPTSE’s national economic effects and spatial equity implications using input–output (IO) analysis. A demand-side IO model—calibrated to 2022 national tables with a novel transmission and substation investment sector—disaggregates investments across five key sectors and estimates production, value-added, wage, and employment multipliers, complemented by multiregional spatial analysis of high-voltage direct or alternating current corridors. The results project KRW 128.2 trillion (USD 92.2 billion) total production, KRW 54.1 trillion (USD 38.9 billion) value-added, KRW 30.9 trillion (USD 22.2 billion) wages, and 578,000 jobs over 2025–2038, with coastal generation regions bearing infrastructure burdens while benefits accrue nationally. The findings demonstrate transmission investments as macroeconomic catalysts, highlighting the need for regionally differentiated compensation addressing land-use conflicts along export or transit corridors. Full article

District energy is one of the most efficient heat distribution systems. The interface between the pipe network and buildings is made of thermal and hydraulic separation units named stations. The control of temperature on the secondary side is handled in substations. Several parameters influence control stability, such as differential pressure, mass flow, temperatures, valve inherent characteristics and controller tuning. There are different design approaches for stations in different geographies. However, one option is a generalist control loop setup, which is analysed here. Four sites in Sweden were monitored for performance (during the winter period and with the same hardware setups), and an analysis of the variability of controller tuning parameters was performed. For the purposes of laboratory comparison, the tests were executed with different configurations of generic control loop setups. The results, arranged into distribution histograms, show similarities between the laboratory and field setups. One can see that well-performing setups are close to a normal distribution, while the others are not. One key parameter is the controller setup and algorithm used. Proper tuning of the controller, together with differential pressure control, secures optimal performance of district energy stations. District heating stations with operations closer to the set point positively influence the performance of the whole grid and therefore improve the energy efficiency of the stations. Full article

Offshore wind (OSW) energy represents a vast and largely untapped resource capable of significantly contributing to the rising global electricity demand while advancing ambitious decarbonization and clean energy transition goals. Despite its potential, the effective harnessing of OSW is contingent upon the strategic and reliable integration of offshore generation into existing onshore AC power systems. Multi-terminal high-voltage direct current (MTDC) networks have emerged as a promising solution for this task, offering enhanced flexibility, scalability, and operational resilience. However, several technical and operational challenges—such as lack of standardization, coordinated control of multiple multi-vendor converters, reliable communication infrastructures, protection schemes, and seamless integration of offshore HVDC substations—must be addressed to fully realize the benefits of MTDC systems. This review paper critically examines these challenges and proposes a control, communication, protection, and HVDC substation design that could be adopted as an initial guideline for the efficient and secure integration of OSW into AC grids. By identifying current research gaps and synthesizing existing solutions, the paper provides a comprehensive framework for optimizing the role of MTDC networks in future offshore wind deployments. Full article

Substations’ auxiliary systems support the station’s operational loads and are crucial for grid security, often requiring backup power to ensure uninterrupted operation. A new alternative for this backup power supply is a microgrid composed of photovoltaic (PV) generation and storage. This paper proposes an electric–hydrogen microgrid as backup power supply for substation auxiliary systems. This microgrid ensures power supply during emergencies, provides clean and stable energy for daily operations, and enhances environmental friendliness and profitability. Firstly, using a 220 kV substation as an example, the construction principles of the proposed backup power microgrid are introduced. Secondly, operation strategies under different scenarios are proposed, considering time-sharing tariffs and different weather conditions. Following this, the capacity configuration optimization model of the electric–hydrogen microgrid is proposed, incorporating critical thresholds for energy reserves to ensure system robustness under fault conditions. Finally, the Particle Swarm Optimization (PSO) algorithm is used to solve the problem, and a sensitivity analysis is performed on hydrogen market pricing to evaluate its impact on the system’s economic feasibility. The results indicate that the proposed electric–hydrogen microgrid is more economical and provides better fault power supply time than battery-only power supply. With the development of hydrogen energy storage technology, the economy of the proposed microgrid is expected to improve further in the future. Full article

To address the challenges posed to the secure and reliable operation of the power grid under the “dual-carbon” goals, an optimal planning and investment return analysis method for grid-side energy storage system (GSESS) is proposed, with multi-dimensional grid security requirements being considered. By this method, a decision-making framework for the scientific planning of GSESS is provided, through which both technical and economic viability are balanced. Firstly, an evaluation indicator system for GSESS demand is established, in which loading stress, voltage quality, and renewable energy accommodation capacity are comprehensively considered. The candidate sites are then prioritized by a hybrid subjective-objective weighting method combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Subsequently, the top 10% most severe scenarios are identified from historical operational data, and a set of typical extreme scenarios is extracted using an improved K-means clustering algorithm. Based on these scenarios, an optimal capacity planning model incorporating multi-dimensional security constraints is formulated, and the final planning scheme is thereby determined. Furthermore, with the objective of maximizing net revenue from multiple application scenarios, an optimal operational model for GSESS is established. The life-cycle costs and benefits are quantified, and a comprehensive investment return analysis is conducted accordingly. Finally, the proposed methodology is validated through a case study based on the 220 kV substations in QZ City of China. It is demonstrated by the results that through the application of the derived planning scheme, the operational security of the power grid is significantly enhanced, and a promising outlook for investment returns is also exhibited. Full article

Against the backdrop of the global energy crisis and the “dual carbon” goals (carbon peaking and carbon neutrality), the passive energy-saving design of substation buildings in cold regions faces severe challenges. This study systematically conducts a decomposed analysis of the shape coefficient, thermal performance of the building envelope (including external walls, internal walls, roofs, and external windows), and window-to-wall ratio of substation buildings in cold regions, quantifies the degree of influence of each factor, and proposes corresponding energy-saving design strategies. This study took a 110 kV substation in Yuhua District, Shijiazhuang City, Hebei Province, as the research object. A building energy consumption model was established based on DeST (2023) software, and the influence of the building shape coefficient, U-values of the envelope structure (external walls, internal walls, roofs, external windows), and window-to-wall ratio on the building’s cooling and heating loads was analyzed using the numerical simulation and control variable methods. Leveraging a rigorously validated, high-resolution simulation framework, we quantitatively dissect the marginal energy penalties and payoffs of every passive design variable governing fully indoor substations in cold-climate zones. The resultant multidimensional response surfaces are distilled into a deterministic, climate-specific passive energy-saving protocol that secures heating-energy savings of up to 43% without compromising electrical safety or operational accessibility. (1) Reducing the shape coefficient can significantly lower the heat load, and it is recommended to control it at 0.35–0.40; (2) The thermal performance of the envelope structure has a differential effect: the energy-saving effect is optimal when the U-value of external walls is 0.20–0.30 W/(m²·K) and the U-value of roofs is ≤0.25 W/(m²·K). A U-value of 2.4 W/(m²·K) is recommended for external windows, while the internal wall exerts a weak influence; (3) The window-to-wall ratio should be controlled by orientation: east-facing/north-facing ≤ 0.20, south-facing ≤ 0.35, and west-facing ≤ 0.30. Based on the above results, a comprehensive energy-saving strategy of “compact form–high-efficiency envelope–limited window-to-wall ratio” is proposed, which provides theoretical support and technical pathways for the energy-saving design of substation buildings in cold areas. Compared with existing substation buildings, the recommended parameters yield a significant reduction in total life-cycle carbon emissions and hold important practical significance for realizing the “dual carbon” goals (carbon peaking and carbon neutrality) of the power system. Full article

The cyber–physical integrated power distribution system is poised to become the predominant trend in the development of future power systems. Although the highly intelligent panoramic link information system in substations facilitates the efficient, cost-effective, and secure operation of the power system, it is also exposed to dual threats from both internal and external factors. Under intentional cyber information attacks, the operational data and equipment response capabilities of the panoramic link information system within smart substations can be illicitly manipulated, thereby disrupting dispatcher response decision-making and resulting in substantial losses. To tackle this challenge, this paper delves into the research on automatic verification and active defense mechanisms for the cyber–physical power distribution system under panoramic link attacks in smart substations. Initially, to mitigate internal risks stemming from the uncertainty of new energy output information, this paper utilizes a CGAN-IK-means model to generate representative scenarios. For scenarios involving external intentional cyber information attacks, this paper devises a fixed–flexible adjustment resource response strategy, making up for the shortfall in equipment response capabilities under information attacks through flexibility resource regulation. The proposed strategy is assessed based on two metrics, voltage level and load shedding volume, and computational efficiency is optimized through an enhanced firefly algorithm. Ultimately, the efficacy and viability of the proposed method are verified and demonstrated using a modified IEEE standard test system. Full article

Modern networked industrial applications often require low-latency communication. Some applications evolve over time, however, are tied to yet existing infrastructures, like power grids spanning across large areas. For instance, medium voltage direct current (MVDC) grids are evolving to a promising alternative to traditional medium voltage alternating current (MVAC) grids due to their efficiency and suitability for novel use cases like electric mobility. MVDC grids, however, require an active control and fault handling strategy. Some strategies demand for a continuous state exchange of the converter substations via a low-latency communication channel with less than 1 millisecond. While some communication approaches for MVDC grids are described in the literature, none of them is inherently designed to be secure. In this paper, we present a protocol for ultra-low-latency secure state exchange (PULLSE) based on conventional non-deterministic Ethernet and AES-GCM. We chose Ethernet in order to not limit the approaches usability in terms of hardware requirements or communication patterns. PULLSE is designed to prevent traffic eavesdropping, replay, and manipulation attacks. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

Recent advances in technologies such as blockchain, the Internet of Things (IoT), Cyber–Physical Systems (CPSs), and the Industrial Internet of Things (IIoT) have driven the digitalization and intelligent transformation of modern industries. However, embedded control devices within power system communication infrastructures have become increasingly susceptible to cyber threats due to escalating software complexity and extensive network exposure. We have seen that symmetric conventional patching techniques—both static and dynamic—often fail to satisfy the stringent requirements of real-time responsiveness and computational efficiency in resource-constrained environments of all kinds of power grids. To address this limitation, we have proposed a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method has integrated binary-level vulnerability modeling, execution-trace-driven fault localization, and lightweight patch synthesis, enabling dynamic, in-place code redirection without disrupting ongoing operations. By constructing a system-level instruction flow model, the framework has leveraged on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture have revealed that the proposed approach has reduced patch latency by 92% over static techniques, which are symmetrical in a working way, while incurring less than 3% CPU overhead. This work has offered a scalable and real-time model-driven defense strategy that has enhanced the cyber–physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability. Full article

This paper introduces an Enhanced-Dueling Deep Q-Network (EDDQN) specifically designed to bolster the physical security of electric power substations. We model the intricate substation security challenge as a Markov Decision Process (MDP), segmenting the facility into three zones, each with potential normal, suspicious, or attacked states. The EDDQN agent learns to strategically select security actions, aiming for optimal threat prevention while minimizing disruptive errors and false alarms. This methodology integrates Double DQN for stable learning, Prioritized Experience Replay (PER) to accelerate the learning process, and a sophisticated neural network architecture tailored to the complexities of multi-zone substation environments. Empirical evaluation using synthetic data derived from historical incident patterns demonstrates the significant advantages of EDDQN over other standard DQN variations, yielding an average reward of 7.5, a threat prevention success rate of 91.1%, and a notably low false alarm rate of 0.5%. The learned action policy exhibits a proactive security posture, establishing EDDQN as a promising and reliable intelligent solution for enhancing the physical resilience of power substations against evolving threats. This research directly addresses the critical need for adaptable and intelligent security mechanisms within the electric power infrastructure. Full article

State evaluation of relay protection equipment constitutes a crucial component in ensuring the stable, secure, and symmetric operation of power systems. Current methodologies predominantly encompass fuzzy-rule-based control systems and data-driven machine learning approaches. The former relies on manual experience for designing fuzzy rules and membership functions and exhibits limitations in high-dimensional data integration and analysis. The latter predominantly formulates state evaluation as a classification task, which demonstrates its ineffectiveness in identifying equipment at boundary states and faces challenges in model parameter selection. To address these limitations, this paper proposes a quantitative state evaluation method for relay protection equipment based on a two-stage artificial protozoa optimizer (two-stage APO) optimized improved Conformer (two-stage APO-IConf) model. First, we modify the Conformer architecture by replacing pre-layer normalization (Pre-LN) in residual networks with post-batch normalization (post-BN) and introducing dynamic weighting coefficients to adaptively regulate the connection strengths between the first and second feed-forward network layers, thereby enhancing the capability of the model to fit relay protection state evaluation data. Subsequently, an improved APO algorithm with two-stage optimization is developed, integrating good point set initialization and elitism preservation strategies to achieve dynamic equilibrium between global exploration and local exploitation in the Conformer hyperparameter space. Experimental validation using operational data from a substation demonstrates that the proposed model achieves a RMSE of 0.5064 and a MAE of 0.2893, representing error reductions of 33.6% and 35.0% compared to the baseline Conformer, and 9.1% and 15.2% error reductions over the improved Conformer, respectively. This methodology can provide a quantitative state evaluation and guidance for developing maintenance strategies for substations. Full article

The growing scale of offshore wind farms and increasing transmission distances has driven the demand for optimized offshore substation (OSS) configurations. This study proposes a comprehensive techno-economic framework to minimize the total lifecycle cost (LCC) of an OSS by determining the optimal number of OSSs and transformers considering wind farm capacity and transmission distance. The methodology incorporates three cost models: capital expenditure (CAPEX), operational expenditure (OPEX), and expected energy not supplied (EENS). CAPEX considers transformer costs, topside structural mass effects, and nonlinear installation costs. OPEX accounts for substation maintenance and vessel operating expenses, and EENS is calculated using transformer failure probability models and redundancy configurations. The optimization is performed through scenario-based simulations and a net present value (NPV)-based comparative analysis to determine the cost-effective configurations. The quantitative analysis demonstrates that for small- to medium-scale wind farms (500–1000 MW), configurations using 1–2 substations and 3–4 transformers achieve minimal LCC regardless of the transmission distance. In contrast, large-scale wind farms (≥1500 MW) require additional substations to mitigate transmission losses and disruption risks, particularly over long distances. These results demonstrate that OSS design should holistically balance initial investment costs, operational reliability, and supply security, providing practical insights for cost-effective planning of next-generation offshore wind projects. Full article