Search Results (84)

In light of the increasing frequency and intensity of natural phenomena, whether climatic or telluric, the relevance of multi-risk assessment approaches has become an important issue for understanding and estimating the impacts of disasters on complex socioeconomic systems. Two aspects contribute to the worsening of this situation. First, climate change has heightened the incidence and, in conjunction, the seriousness of geohazards that often occur with each other. Second, the complexity of these impacts on societies is drastically exacerbated by the interconnections between urban areas, industrial sites, power or water networks, and vulnerable ecosystems. In front of the recent research on this problem, and the necessity to figure out the best scientific positioning to address it, we propose, through this review analysis, to revisit existing literature on multi-risk assessment methodologies. By this means, we emphasize the new recent research frameworks able to produce determinant advances. Our selection corpus identifies pertinent scientific publications from various sources, including personal bibliographic databases, but also OpenAlex outputs and Web of Science contents. We evaluated these works from different criteria and key findings, using indicators inspired by the PRISMA bibliometric method. Through this comprehensive analysis of recent advances in multi-risk assessment approaches, we highlight main issues that the scientific community should address in the coming years, we identify the different kinds of geohazards concerned, the way to integrate them in a multi-risk approach, and the characteristics of the presented case studies. The results underscore the urgency of developing robust, adaptable methodologies, effectively able to capture the complexities of multi-risk scenarios. This challenge should be at the basis of the keys and solutions contributing to more resilient socioeconomic systems. Full article

Large-scale integration of renewable energy introduces significant random perturbations to the power system, disrupting the symmetry and balance of active power, which complicates the stabilization of the system’s frequency. Inter-regional energy cooperation plays a crucial role in maintaining the symmetry and balance of the overall power system’s active power. However, when the power system area is expanded, automatic generation control (AGC) based on reinforcement learning faces the challenge of not being able to leverage the prior experience of the original system topology to train the new area, making it difficult to quickly develop an effective control strategy. To address these challenges, this paper proposes a novel data-driven AGC method that employs a multi-agent reinforcement learning algorithm with a learning to coordinate and teach reinforcement (LECTR) mechanism. Specifically, under the LECTR mechanism, when the power system region expands, agents in the original region will instruct the agents in the newly merged region by providing demonstration actions. This accelerates the convergence of their strategy networks and improves control accuracy. Additionally, the proposed algorithm introduces a double critic network to mitigate the issue of target critic network value overestimation in reinforcement learning, thereby obtaining higher-quality empirical data and improving algorithm stability. Finally, simulations are conducted to evaluate the method’s effectiveness in scenarios with an increasing number of IEEE interconnected grid areas. Full article

By using a soft normal open point (SNOP) to connect multiple distribution networks to form a flexible interconnected distribution system (FIDS), the power distribution can be flexibly and controllably regulated among distribution stations, but it is also necessary to ensure the system’s operational efficiency and maintain voltage quality when carrying out optimal scheduling. In this paper, a FIDS optimal scheduling strategy considering dynamic weight grading is proposed. By considering the voltage overrun status of each distribution station area, the voltage level of each distribution station area is divided into three voltage overrun situations, including normal operation, safe boundary, and protection boundary levels, and an optimal scheduling model applicable to the multi-level operation of the FIDS is constructed. In order to adapt to the coordinated optimal operation objectives under different overrun levels, an optimal operation strategy considering the dynamic weights of system operation cost, voltage deviation, customer satisfaction, and SNOP regulation capability is proposed and finally simulated and verified using the improved IEEE33 node arithmetic case. The results verify the effectiveness of the method proposed in this paper in improving the system’s operational efficiency and node voltage quality. Full article

The rapid integration of renewable energy sources and the increasing complexity of energy demands necessitate advanced strategies for optimizing multi-region energy systems. This study investigates the coordinated energy management of interconnected parks by incorporating wind power, demand response (DR) mechanisms, and energy storage systems. A comprehensive optimization framework is developed to enhance energy sharing among parks, leveraging demand-side flexibility and renewable energy integration. Simulation results demonstrate that the proposed approach significantly improves system efficiency by balancing supply-demand mismatches and reducing reliance on external power sources. Compared to conventional methods, the DR capabilities of industrial and commercial loads have increased by 8.08% and 6.69%, respectively, which is primarily due to enhanced utilization of wind power and optimized storage deployment. The inclusion of DR contributed to improved system flexibility, enabling a more resilient energy exchange framework. This study highlights the potential of collaborative energy management in multi-area systems and provides a pathway for future research to explore advanced control algorithms and the integration of additional renewable energy sources. Full article

This study develops a novel Lyrebird Optimization Algorithm (LOA), a technique inspired by the wild behavioral strategies of lyrebirds in response to potential threats. In a two-area interconnected power system that includes non-reheat thermal stations, this algorithm is applied to handle load frequency control (LFC) by optimizing the parameters of a Proportional–Integral–Derivative controller with a filter (PIDn). This study incorporates generation rate constraints (GRCs). The efficiency of the provided LOA-PIDn is evaluated through simulations under various disturbance scenarios and is compared against other well-established optimization techniques, including the Ziegler–Nichols (ZN), genetic algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Firefly Approach (FA), hybridized FA and pattern search (hFA–PS), self-adaptive multi-population elitist Jaya (SAMPE-Jaya)-based PI/PID controllers, and Teaching–Learning-Based Optimizer (TLBO) IDD/PIDD controllers. The results demonstrate the LOA’s ability to minimize the integral of time multiplied by absolute error (ITAE) and achieve significantly lower settling times for the two-area frequencies and transferred power variances in comparison with other methods. The comprehensive comparison and the inclusion of real-world constraints validate the LOA as a robust and effective tool for addressing complex optimization challenges in modern power systems. Full article

Urban infrastructure, the lifeline of modern society, consists of inherently multidimensional and interdependent systems that extend beyond various engineered facilities, utilities, and networks. The increasing frequency of extreme events, like floods, typhoons, power outages, and technical failures, has heightened the vulnerability of these infrastructures to cascading disasters. Over the past decade, significant attention has been devoted to understanding urban infrastructure cascading disasters. However, most of them have been limited by one-sided and one-dimensional analyses. A more systematic and scientific methodology is needed to comprehensively profile existing research on urban infrastructure cascading disasters to address this gap. This paper uses scientometric methods to investigate the state-of-the-art research in this area over the past decade. A total of 165 publications from 2014 to 2023 were retrieved from the Web of Science database for in-depth analysis. It has revealed a shift in research focus from single infrastructures to complex, interconnected systems with multidimensional dependencies. In addition, the study of disaster-causing factors has evolved from internal infrastructure failures to a focus on cascading disasters caused by extreme events, highlighting a trend of multi-factor coupling. Furthermore, predicting and modeling cascading disasters, improving infrastructure resilience, and information sharing for collaborative emergency responses have emerged as key strategies in responding to disasters. Overall, the insights gained from this study enhance our understanding of the evolution and current challenges in urban infrastructure cascading disasters. Additionally, this study offers valuable perspectives and directions for policymakers addressing extreme events in this critical area. Full article

In modern power systems, fluctuations in load present ongoing challenges, making Load frequency control (LFC) an essential part of maintaining system stability and efficiency. This paper explores a method that combines traditional PID control with the Particle Swarm Optimization (PSO) algorithm to improve frequency regulation in interconnected hydropower systems. By using PSO, the method fine-tunes the PID controller parameters, enhancing frequency regulation, accelerating stabilization, and ensuring steady power flow across interconnecting lines. The simulation results show that this optimized method outperforms the conventional techniques, offering improved dynamic responses and a more robust performance, even in challenging and variable conditions. This makes it a promising solution for modern power systems, particularly in managing load fluctuations and maintaining frequency stability in interconnected hydropower systems. Full article

A virtual power plant (VPP) is a potential alternative that aggregates the distributed energy resources (DERs) and addresses the prosumer’s power availability, quality, and reliability requirements. This paper reports the optimized scheduling of an interconnected VPP in a multi-area framework established through a tie-line connection comprising multiple renewable resources. The scheduling was initially performed on a day ahead (hourly basis) interval, followed by an hour ahead interval (intra-hour and real time), i.e., a 15 min and 5 min time interval for the developed VPP in a multi-area context. The target objective functions for the selected problem were two-fold, i.e., net profit and emission, for which maximization was performed for the former and reduction for the later, respectively. Since renewables are involved in the energy mix and the developed problem was complex in nature, the proposed multi-area-based VPP was tested with an advanced nature-inspired metaheuristic technique. Moreover, the proposed formulation was extended to a multi-objective context, and multiple scheduling strategies were performed to reduce the generated emissions and capitalize on the cumulative profit associated with the system by improving the profit margin simultaneously. Furthermore, a comprehensive numeric evaluation was performed with different optimization intervals, which revealed the rapid convergence in minimal computational time to reach the desired solution. Full article

The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of power networks. Nonetheless, its extensive implementation confronts constraints, including expense, life expectancy, and energy efficiency. Simultaneously, these technologies present prospects for improved energy management, increase the hosting capacity of renewable energy, and diminish reliance on fossil fuels. This paper investigates the obstacles of integrating electrochemical storage into electrical power systems, explores solutions to use its promise for creating more resilient and sustainable grids, and presents a method for the size estimation and strategic allocation of electrochemical energy storage systems (EESSs). The aim is to improve grid voltage profiles, manage demand response, increase the adoption of renewable energy resources, enhance power transfer among various areas, and subsequently improve the stability of a power system during large disturbances. The methodology utilizes a multi-stage optimization process based on economic considerations supported by dynamic simulation. This methodology was tested employing a validated dynamic model of the Interconnected Electrical System of the Central American Countries (SIEPAC). The system experienced multiple significant blackouts in recent years, primarily due to the increasing amount of renewable energy generation without adequate inertial support and limited power transfer capabilities among countries. Based on the results of using the technique, EESSs can effectively lower the risk of instability caused by an imbalance between power generation and demand during extreme situations, as seen in past event reports. Based on economical constraints, it has been determined that the cost of installing EESSs for the SIEPAC, which amounts to 1200 MWh/200 MW, is 140.91 USD/MWh. Full article

Wind–solar power generating and hybrid battery-supercapacitor energy storage complex is used for autonomous power supply of consumers in remote areas. This work uses passivity-based control (PBC) for this complex in accordance with the accepted energy management strategy (EMS). Structural and parametric synthesis of the overall PBC system was carried out, which was accompanied by a significant amount of research. In order to simplify this synthesis, a structural decomposition of the overall dynamic system of the object presented in the form of a port-Hamiltonian system, which was described by a system of differential equations of the seventh order, into three subsystems was applied. These subsystems are a wind turbine, a PV plant, and a hybrid battery-supercapacitor system. For each of the subsystems, it is quite simple to synthesize the control influence formers according to the interconnections and damping assignment (IDA) method of PBC, which locally performs the tasks set by the EMS. The results obtained by computer simulation of the overall and decomposed systems demonstrate the effectiveness of this approach in simplifying synthesis and debugging procedures of complex multi-physical systems. Full article

With the proposed “double carbon” target for the power system, large-scale distributed energy access poses a major challenge to the way the distribution grid operates. The rural distribution network (DN) will transform into a new local power system primarily driven by distributed renewable energy sources and energy storage, while also being interconnected with the larger power grid. The development of the rural DN will heavily rely on the construction and efficient planning of the microgrid (MG) within the agricultural park. Based on this, this paper proposes a two-stage optimal scheduling model and solution strategy for the microgrid distribution network with multi-source agricultural load aggregation. First, in the first stage, considering the flexible agricultural load and the market time-of-use electricity price, the economic optimization is realized by optimizing the operation of the schedulable resources of the park. The linear model in this stage is solved by the Lingo algorithm with fast solution speed and high accuracy. In the second stage, the power interaction between the MG and the DN in the agricultural park is considered. By optimising the output of the reactive power compensation device, the operating state of the DN is optimised. At this stage, the non-linear and convex optimization problems are solved by the particle swarm optimization algorithm. Finally, the example analysis shows that the proposed method can effectively improve the feasible region of safe operation of the distribution network in rural areas and improve the operating income of a multi-source agricultural load aggregation agricultural park. Full article

The traditional mathematical models for solving the economic dispatch problem at the generation level primarily focus on minimizing overall operational costs while ensuring demand is met across various periods. However, contemporary power systems integrate a diverse mix of generators from both conventional and renewable energy sources, contributing to economically efficient energy production and playing a pivotal role in reducing greenhouse gas emissions. As the complexity of power systems increases, the scope of economic dispatch must expand to address demand across multiple regions, incorporating a range of objective functions that optimize energy resource utilization, reduce costs, and achieve superior economic and technical outcomes. This paper, therefore, proposes an advanced optimization model designed to determine the hourly power output of various generation units distributed across multiple areas within the power system. The model satisfies the dual objective functions and adheres to stringent technical constraints, effectively framing the problem as a nonlinear programming challenge. Furthermore, an in-depth analysis of the resulting and exchanged energy quantities demonstrates that the model guarantees the hourly demand. Significantly, the system’s efficiency can be further enhanced by increasing the capacity of the interconnection links between areas, thereby generating additional savings that can be reinvested into expanding the links’ capacity. Moreover, the multi-objective model excels not only in meeting the proposed objective functions but also in optimizing energy exchange across the system. This optimization is applicable to various types of energy, including thermal and renewable sources, even those characterized by uncertainty in their primary resources. The model’s ability to effectively manage such uncertainties underscores its robustness, instilling confidence in its applicability and reliability across diverse energy scenarios. This adaptability makes the model a significant contribution to the field, offering a sophisticated tool for optimizing multi-area power systems in a way that balances economic, technical, and environmental considerations. Full article

As the global demand for energy sustainability increases, the scale of wind power integration steadily increases, so the system frequency suffers significant challenges due to the huge fluctuations of the wind power output. To address this issue, this paper proposes a Back Propagation Neural Network-Proportional-Integral-Derivative (BPNN-PID) controller to track the output power of the wind power generation system, which can well alleviate the volatility of the wind power output, resulting in the slighter imbalance with the rated wind power output. Furthermore, at the multi-area power system level, to mitigate the impact of the imbalanced wind power injected into the main grid, the H_∞ robust controller was designed to ensure the frequency deviation within the admissible range. Finally, a four-area interconnected power system was employed as the test system, and the results validated the feasibility and effectiveness of both the proposed BPNN-PID controller and the robust controller. Full article

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources. Full article

Multi-motor servo systems are widely used in industrial control. However, the single-core microprocessor architecture based on the microcontroller unit (MCU) and digital signal processor (DSP) is not well suited for high-performance multi-motor servo systems due to the inherent limitations in computing performance and serial execution of code. The bus-based distributed architecture formed by interconnecting multiple unit controllers increases system communication complexity, reduces system integration, and incurs additional hardware and software costs. Field programmable gate array (FPGA) possesses the characteristics of high real-time performance, parallel processing, and modularity. A single FPGA can integrate multiple motor servo controllers. This research uses MCU + FPGA as the core to realize high-precision multi-axis real-time control, combining the powerful performance of the MCU processor and the high-speed parallelism of FPGA. The MCU serves as the central processor and facilitates data interaction with the host computer through the controller area network (CAN). After data parsing and efficient computation, MCU communicates with the FPGA through flexible static memory controller (FSMC). A motor servo controller intellectual property (IP) core is designed and packaged for easy reuse within the FPGA. A 38-axis micro direct current (DC) motor control system is constructed to test the performance of the IP core and the heterogeneous embedded platforms. The experimental results show that the designed IP core exhibits robust functionality and scalability. The system exhibits high real-time performance and reliability. Full article