Search Results (27)

The regional integrated energy system (RIES) has emerged as a critical focus in energy systems research. The comprehensive incorporation of renewable energy and inherent multi-energy flow interconnection within RIES markedly elevates the complexity of “generation-load” balance regulation. Traditional model-driven dynamic state estimation methods, however, are constrained by fundamental limitations—complex modeling, inadequate representation of multi-energy flow interdependencies, and poor computational efficiency. This study proposes a data-driven dynamic state estimation method for RIES, utilizing multi-dimensional “generation-grid-load” characteristic information as its primary input and employing a synergistic framework of Empirical Mode Decomposition-Singular Value Decomposition (EMD-SVD) alongside an enhanced Bidirectional Long Short-Term Memory (BiLSTM) network. EMD-SVD preprocesses raw data to remove noise and extract essential features, while the enhanced BiLSTM serves a dual purpose: it first attains high-precision photovoltaic output prediction and multi-energy load forecasting and subsequently evaluates the node states of the multi-energy flow coupling system. A case study on a practical coupled RIES, comprising a 33-node power system, 7-node gas system, and 6-node thermal system, demonstrates that the proposed method achieves high estimation accuracy and remarkable computational efficiency while effectively addressing the inherent limitations of conventional model-driven approaches. Full article

Hydrogen energy is developing rapidly, and the hydrogen-based integrated energy system (HIES) offers improved economic performance, flexibility, and environmental benefits compared with conventional power systems. However, the increasing frequency of natural disasters caused by climate change introduces significant vulnerabilities that threaten system security. Preventive scheduling provides a proactive and economical means to enhance system resilience against such uncertainties. This paper proposes a preventive scheduling model for HIES based on adaptive robust optimization (ARO) to address the uncertain impacts of natural disasters on transmission lines, pipelines, and roads. The model incorporates the operational constraints and interdependencies among multiple energy subsystems and integrates flexible scheduling strategies such as power-to-hydrogen-and-heat (P2HH) and hydrogen transportation (HT). A hybrid algorithm is developed to efficiently solve the large-scale ARO problem with numerous integer variables. Case studies performed on two test systems demonstrate that the proposed preventive scheduling model effectively reduces operational costs and load curtailments. Simulation results show that coordinating P2HH and HT reduces power, heat, hydrogen, and gas load curtailments by 14.35%, 43.39%, 49.97%, and 40.32%, respectively, as well as operational costs by 14.60%. Moreover, the proposed hybrid algorithm enhances computational efficiency, reducing solution time by 21% with only a 2% deviation from the solution obtained by the conventional C&CG–AOP algorithm. Full article

Natural gas plays a critical role in integrated energy systems. In this context, the present study proposes an optimization model for the electricity–gas coupling system, grounded in the theory of interdependent networks. By integrating network topology parameters with real-time operational metrics, the model substantially enhances system robustness and adaptability. To quantify nodal vulnerability and importance, the study introduces two novel evaluation indicators: the Electric Potential–Closeness Fusion Indicator (EPFI) for power networks and the Pressure Difference–Closeness Comprehensive Indicator (PDCI) for natural gas systems. Leveraging these indicators, three coupling paradigms—assortative, disassortative, and random—are systematically constructed and analyzed. System resilience is assessed through simulation experiments incorporating three attack strategies: degree-based, betweenness centrality-based, and random node removal. Evaluation metrics include network efficiency and the variation in the size of the largest connected subgraph under different coupling configurations. The proposed framework is validated using a hybrid case study that combines the IEEE 118-node electricity network with a 20-node Belgian natural gas system, operating under a unidirectional gas-to-electricity energy flow model. Results confirm that the disassortative coupling configuration, based on EPFI and PDCI indicators, exhibits superior resistance to network perturbations, thereby affirming the effectiveness of the model in improving the robustness of integrated energy systems. Full article

To reduce carbon emissions from fossil fuel generators in sustainable energy systems, an option is increasing the integration of gas-fired generators into the power system. The increasing reliance on natural gas for electricity generation has strengthened the interdependence between the electric power network and the natural gas infrastructure within the Integrated Power and Gas System (IPGS). This strengthened interdependence increases the risk that disruptions originating in one system may propagate to the other, potentially leading to extensive cascading failures throughout the IPGS. Ensuring the reliability of critical energy infrastructure is vital for sustainable development. This paper proposes a vulnerability assessment method for the IPGS using an influence graph, which can be formulated based on fault chain theory to capture the interactions among failed components in the IPGS. With the influence graph, eigenvector centrality is used to pinpoint the critical components in the IPGS. The proposed methodology is validated using 39-bus 29-node IPGS through the Scenario Analysis Interface for Energy Systems (SAInt) software version 3.5.17.7. Results show that the proposed method has effectively identified the most critical branches in the IPGS, which play a key role in initiating cascading failures. These insights contribute to enhancing the resilience and sustainability of interconnected energy systems. Full article

The global transition toward low-carbon energy systems necessitates fundamental innovations in demand-side flexibility, particularly in industrial load regulation. This study presents a systematic review and critical analysis of 90 key research works (2015–2025) to establish a comprehensive framework for industrial load flexibility enhancement. We rigorously examined the tripartite interdependencies among the following: (1) Multi-energy flow physical coupling, addressing temporal-scale disparities in electricity-thermal-gas coordination under renewable penetration; (2) Uncertainty quantification, integrating data-driven and physics-informed modeling for robust decision-making; (3) Market mechanism synergy, analyzing demand response, carbon-P2P hybrid markets, and regulatory policy impacts. Our analysis reveals three fundamental challenges: the accuracy-stability trade-off in cross-timescale optimization, the policy-model disconnect in carbon-aware scheduling, and the computational complexity barrier for real-time industrial applications. The paper further proposes a roadmap for next-generation industrial load regulation systems, emphasizing co-optimization of technical feasibility, economic viability, and policy compliance. These findings advance both academic research and practical implementations for carbon-neutral power systems. Full article

Cement bond quality is critical to ensuring the long-term safety and structural integrity of oil and gas wells. However, due to the complex interdependencies among geological conditions, operational parameters, and fluid properties, accurately predicting cement bond quality remains a considerable challenge. To improve the accuracy and practical applicability of cement bond prediction, this study develops an intelligent prediction model. A Wide and Deep neural network architecture is adopted, into which two key parameters of the cement slurry’s power-law rheological model—the consistency coefficient and the flow behavior index—are embedded. A temperature correction mechanism is incorporated by integrating the correction equations directly into the network structure, allowing for a more realistic representation of the cement slurry’s behavior under downhole conditions. The proposed model is designed to simultaneously predict the bonding quality at both the casing–cement sheath and cement sheath–formation interfaces. It is trained on a field dataset comprising 30,000 samples from eight wells in an oilfield in western China. On the test set, the model achieved prediction accuracies of 87.29% and 87.49% at the two interfaces, respectively. Furthermore, field testing conducted during a third-stage cementing operation of a well demonstrated a prediction accuracy of approximately 90%, indicating strong adaptability to real-world engineering conditions. The results demonstrate that the temperature-corrected neural network effectively captures the flow characteristics of the cement slurry. The proposed model meets engineering application requirements and serves as a reliable, data-driven tool for optimizing cementing operations and enhancing well integrity. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

The relationships between the prices of major energy commodities have been a widely discussed topic in energy market analyses. This study examines whether the substantial changes observed in recent years have influenced the price linkages between coal and natural gas. By comparing selected price indices from European and Asian markets, we assess the evolving interdependencies between these fuels. The results indicate that the most significant changes in price linkages have occurred in European markets. Both VAR and ARDL model-based tests reveal a shift in the direction of causal relationships. Between 2006 and 2011, coal prices significantly influenced natural gas prices, with no strong evidence of reverse causality. However, in the more recent period (2018–2023), the relationship reversed—natural gas prices now have a significant impact on coal prices, while the reverse linkage has weakened. In Asian markets, the changes were less pronounced, particularly for Japanese import gas prices based on lagged average formulas. However, in the most recent period, a notable influence of Indonesian import gas prices on Australian coal prices emerged, mirroring trends observed in Europe. These findings highlight the increasing role of natural gas in shaping energy commodity prices, especially in Europe, where its growing importance in power generation has contributed to this shift. Additionally, the post-2018 period has been marked by significant supply disruptions, particularly in Europe, with geopolitical factors playing a crucial role in amplifying the importance of natural gas prices. Full article

The study investigates the impact of water use in energy production in industrial plants, considering the interdependence between water and energy, or the water–energy nexus, to promote sustainable water and energy management. More specifically, it focuses on the industrial sector, particularly on electricity production in thermoelectric power plants, which require large amounts of water for cooling in its production cycle. The field of analysis is set in Italy, referring to the applications of the European Industrial Emissions Directive and Italian regulations that govern water and energy usage. The focus is on large combustion plants, which need to be monitored by national authorities. The Italian situation is outlined, exposing consumption data from major thermoelectric power plants in 2021 through 2023, highlighting the water usage trend and electricity production. In 2023, total water use for these installations was 9,892,719,965 m³—mainly from seawater—with an overall production of electric energy of 117,239,954 MWh, with a relevant fuel consumption from natural gas (18,544,742,774 Sm³). It also analyzed the application of best available techniques to reduce water consumption, recycle water flows, and minimize the environmental impact of power plants. Finally, the main fuels used in these plants, such as natural gas, coal, and biomass, are presented, along with the environmental performance of the power plants based on water use per unit of energy produced. Full article

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact. Full article

This paper addresses the problem of the geothermal energy generation process in a depleted gas reservoir with a specific enhanced geothermal system, applying CO₂ as an energy transporting medium. Constructed models of the system components are used to perform coupled and dynamic simulation forecasts, taking into account the interdependence of the individual system elements operating in a cyclical fluid flow and the continuous changes in temperature, pressure, and the composition of circulating fluids. The simulation procedure of the geothermal energy generation process is applied to the realistic example of a depleted gas reservoir located in Foresudetic Monocline, Poland. The simulation results are presented in detail and discussed with several conclusions of both case-specific and general characters. Three phases of the energy recovery process can be distinguished, varying in the produced fluid composition and the evolution of the fluid temperature. These phases result in the corresponding behavior of the produced stream power: increasing, stable, and decreasing for the three phases, respectively. Other significant results of the simulation forecasts are also discussed and concluded. In general, the complexity of the obtained results proves the necessity to apply the system’s detailed modeling and simulations to reliably plan and realize a geothermal energy generation project. Full article

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds. Full article

This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy system planning strategy that can enhance the systems’ ability to respond to such events. Our strategy unfolds in two stages. Initially, we devise expansion strategies for the interdependent networks through a detailed tri-level planning model, including transmission, generation, and market dynamics within a deregulated electricity market setting, formulated as a mixed-integer linear programming (MILP) problem. Subsequently, we assess the impact of extreme events through worst-case scenarios, applying previously determined network configurations. Finally, the integrated expansion planning strategies are evaluated using real-world test systems. Full article

The growing use of gas-fired power generators and electricity-driven gas compressors and storage has increased the interdependence between electric power infrastructure and natural gas infrastructure. However, the increasing interdependence may spread the failures from one system to the other, causing subsequent failures in an integrated power and gas system (IPGS). This paper investigates the structural vulnerability of a realistic IPGS based on complex network theory. Different from the existing works with a focus on the static vulnerability analysis for an IPGS, this paper considers both static and dynamic vulnerability analysis. The former focuses on vulnerability analysis under random and selective failures without flow redistribution, while the latter concentrates on vulnerability analysis under cascading failures caused by flow redistribution. Also, different from the existing works with a focus on the IPGS as a whole, we not only analyze the vulnerability of the IPGS but also analyze the vulnerability of the power subsystem (PS) and gas subsystem (GS), in order to understand how the vulnerability of the IPGS is affected by its PS and GS. The analysis results show that (1) if the PS and GS are more susceptible to cascading failures than selective and random failures, the IPGS as a whole is also more vulnerable to cascading failures. (2) There are different dominant factors affecting the IPGS vulnerability under cascading failures and selective failures. Under cascading failures, the GS has a more significant impact on the IPGS vulnerability; under selective failures, the PS has a more important impact on the IPGS vulnerability. (3) The IPGS is more vulnerable to failures on the critical nodes, which are identified from the IPGS as a whole rather than from the individual PS or GS. The results provide insights into the design and planning of IPGSs to improve their overall reliability. Full article

In today’s world, the safety, economic prosperity, and social well-being of nations depend heavily on highly interconnected critical infrastructures. These infrastructures encompass power networks, natural gas systems, communication networks, water treatment facilities, and transportation systems. Gaining insight into the behavior of these infrastructures, particularly during stress or attacks, has become crucial for both the private and public sectors. Ensuring an adequate level of functionality during emergencies, such as disasters, is also a priority, which can be attained by enhancing infrastructure resilience. Resilience metrics and models play a significant role in understanding the complex interplay between the behaviors and operational characteristics of interdependent critical infrastructures. Additionally, these models and metrics must demonstrate the interdependencies among infrastructures to provide a more comprehensive representation of infrastructure resilience. This paper reviews, categorizes, and presents resilience metrics and models for Smart Interdependent Critical Infrastructures (Smart ICIs). This paper provides a comprehensive evaluation of various resilience models and measurements tailored specifically for interdependent critical smart infrastructures. It includes the essential terminology and definitions related to the resilience of Smart ICIs, investigates the universally recognized phases and capabilities of resilience, and examines the various types of failures that could potentially affect Smart ICIs. Full article