A dynamic optimization method for a smart grid

Regional rail transit, formed by the economic integration needs of urban agglomerations, is a comprehensive rail transit system with different kinds of transportation systems. Nowadays, although different kinds of rail transit systems are developing rapidly and the facilities are comprehensive, there is a lack of effective dynamic coordination among the various standards and subsystems. Consequently, the regional rail transit network cannot meet higher overall transport capacity and safety requirements. To solve this problem, this paper proposes a dynamic collaborative optimization method for regional rail transit based on passenger route guidance and train scheduling. This method takes the minimization of the maximum peak value of the road network dynamic risk as the optimization objective to establish a mathematical optimization model. The rail transit road network in the Chengdu–Chongqing area is taken as an example to establish a road network simulation model. Through the combination of the simulation model and intelligent optimization algorithms, the entire multi-standard rail transit network is dynamically optimized for train dispatching and passenger flow guidance. Finally, the effectiveness of the method is verified by the simulation of the actual rail transit network.

Dynamic optimization of ship energy efficiency considering time-varying environmental factors

Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket,the vibration acceleration of tool is taken as objective function,and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations,Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method,exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA),is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation,the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimization. Such kind of method can be applied in the design of robots,NC machine,and other electromechanical equipments.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.

A smart grid that integrates additional renewable energy sources (RES) requires advanced management strategies to increase dependability and efficiency. Accurate estimations of energy consumption and RES generation are essential to the functioning of the smart grid. But the evaluation procedure is still difficult due to the unpredictable nature of RES and the diverse energy consumption patterns of users. The research introduced an innovative approach, the modified coot-optimized tuned hierarchical artificial neural network (MCO-HANN), for managing RES in smart grids. To use RES data, with continuous wavelet transform (CWT) applied for pre-processing to extract significant features from energy usage data. A python-based implementation of the MCO-HANN approach is evaluated using various metrics, including RMSE (0.0435), precision (96.2%), recall (95.4%), MAE (0.035), and accuracy (97.6%). The results of a comparative analysis show that the proposed MCO-HANN approach outperforms existing techniques in managing RES within smart grids. However, the model's functionality is influenced by the quality of input data, and its scalability can face challenges when applied to large, real-world smart grid environments. The method offers a viable way to maximize the incorporation of RES in upcoming smart grid systems.

An enhanced scatter search method for the global dynamic optimization of nonlinear processes using the control vector parametrization (CVP) approach is presented. Sharing some features of the scatter search metaheuristic, this new method presents a simpler but more effective design which helps to overcome typical difficulties of nonlinear dynamic systems optimization such as noise, flat areas, nonsmoothness, and/or discontinuities. This new algorithm provides a good balance between robustness and efficiency in the global phase, and couples a local search procedure to accelerate the convergence to optimal solutions. Its application to four multimodal dynamic optimization problems, compared with other state-of-the-art global optimization algorithms, including an advanced scatter search design, proves its efficiency and robustness, showing a very good scalability.

Decision letter for "A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids"

A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

High-performance computing (HPC) systems consume huge amount of power, while bringing about higher operational costs, lower reliability. Power optimization has become an important target for HPC system design. Network energy optimization is significant for the energy optimization of the whole system. In this paper, the dynamic energy optimization method of network with MPI communication operation is presented, which can find MPI operation implementation with the least network energy by accurate dynamic energy estimation, and reduce the energy consumption of network. Taking barrier as an example, we validate the correctness and usefulness of the dynamic energy optimization method of network.

A dynamic performance analysis and optimization method of the horizontal machining center based on contact theory was presented in this paper. The contact stiffness models of ball screw joints and guide joints were proposed based on contact theory, and the predicted values are in good agreement with the experimental results. In particular, the prediction errors of the ball screw joints are within 2.8% compared with the 13 sets of experimental results of THK Company for 26 types. The analysis principle of dynamic performance was given by modal theory. With the goal of reducing the sum of modal flexibility and strengthening the weak modes, static flexibility analysis, modal flexibility analysis, and potential energy distribution analysis were carried out. By studying the effects of geometric parameters, material parameters, surface parameters, and assembly parameters on weak parts, the most effective parameters for optimization were determined. The sensitive parameters were improved from type selection, surface technology, assembly process, and structural improvement. The optimization results showed that the modal flexibility of the weak modes has been reduced by 51.80%, 38.44%, 46.58%, and 27.21%, respectively, the modal flexibility ratios have been reduced by 17.27%, 5.19%, 27.11%, and 2.45%, respectively, and the 1st, 3rd, 4th, and 7th natural frequencies of the horizontal machining center have increased by 26.64%, 11.38%, 4.14%, and 4.55%, respectively. The effects of various parameters on the horizontal machining center were also discussed, which can provide reference for further optimization and research.

Ensemble prediction-based dynamic robust multi-objective optimization methods

With the rapid advancement of technology and the growing reliance on renewable energy sources, smart grids have emerged as a transformative solution for enhancing the efficiency and reliability of power distribution systems. As the smart grid ecosystem evolves, security concerns become paramount due to the interconnectedness of various components, including advanced metering infrastructure, communication networks, and distributed energy resources. This paper presents a comprehensive analysis of smart grid security, focusing on the challenges, vulnerabilities, and potential threats that must be addressed to ensure the resilience of these intelligent infrastructures. It explores the diverse attack vectors that malicious actors may exploit to compromise smart grid operations and highlights the potential consequences of such security breaches, including power disruptions, data breaches, and financial losses. To counteract these risks, this paper delves into various state-of-the-art security measures and strategies that can be implemented to safeguard smart grids effectively. Moreover, the importance of collaboration between various stakeholders is described, including utility companies, government agencies, researchers, and technology vendors, to foster a holistic approach to smart grid security. By emphasizing the significance of information sharing and collaboration, this paper advocates for a robust and adaptive security framework to respond promptly to emerging threats and vulnerabilities. Furthermore, the paper explores emerging technologies such as artificial intelligence, blockchain, and intrusion detection systems, showcasing their potential contributions in fortifying smart grid defenses. It was noted that the integration of these innovative technologies can enhance anomaly detection, facilitate secure data exchange, and reinforce the resilience of the smart grid against sophisticated cyber-attacks. Towards the end of this paper, insights into future research directions and policy implications to foster a more secure and sustainable smart grid environment is provided. By acknowledging the evolving threat landscape and the dynamic nature of the smart grid domain, this work encourages continuous research and development to stay ahead of adversaries and establish a secure foundation for the future of energy distribution.

The steering system has an important impact on the energy consumption, maneuverability, and driving stability of vehicles. In order to improve the system’s comprehensive performance, this research takes electro-hydraulic active steering (EHAS) system as the design object and utilizes the dynamic constraints collaborative optimization (DCCO) method for the parametric optimization of the EHAS system. Steering modality is combined with dynamic performance evaluation indices such as steering energy consumption, steering road feel, steering sensibility, and steering stability, to evaluate the comprehensive performance of the EHAS system. Then, a hierarchical multidisciplinary optimization model of EHAS system is established based on DCCO method, which takes the comprehensive performance of steering system as the overall system and steering energy consumption, steering road feel, steering sensibility, and steering modality as subsystems respectively. The multidisciplinary performance evaluation indices of EHAS system are derived for the first time, among which the evaluation index of steering modality is obtained by combining of design of experiments (DOE) design method and response surface method. The optimization results show that the EHAS system optimized by DCCO method can effectively improve steering energy consumption, steering road feel, and steering sensibility.

An animal dynamic migration optimization method for directional association rule mining

Against the backdrop of a global shift towards renewable energy sources (RES) and the increasing complexity of modern power grids, smart grids (SGs) emerge as a critical solution for enhancing efficiency, reliability, and sustainability. This paper reviews a total of 177 key references published between 2014 and 2024, providing a comprehensive analysis of the most recent advancements in SG technologies and their pivotal role in shaping the future of power systems. The research explores the multifaceted components of SGs, encompassing intelligent sensing, communication technologies, home energy management systems, demand-side management, smart metering, smart electric vehicle charging, smart lighting, the Internet of Things, and the integration of big data, while investigating their role in enhancing the power grid in a smart and digitally integrated manner. The paper also discusses the SG features that have developed and improved the transmission and distribution networks, as well as the ancillary services on a grid-scale introduced through digitization. In addition, the status of the investment in SG is highlighted. The electrification of the global economy, driven by the urgent need to integrate RES, prompted an 8% increase in electricity grid investment in 2022. Additionally, digital technology investments for the grid constituted 19% of the total grid investment in the same year. SGs stand at the forefront of this transformation, playing a central role in facilitating the integration of variable RES, implementing digital technologies, smart devices, and optimizing power systems for diverse sectors. Despite substantial progress, some challenges persist that need further attention to ensure the seamless integration of SG technologies. The findings underscore the pivotal role of SGs in enhancing energy efficiency, effectively managing distributed energy resources, and strengthening the resilience of power systems. In conclusion, the recent advancements in SGs mark a transformative era for the power sector. However, to fully unlock their potential, it is essential to address the key challenges that hinder their seamless integration. Accordingly, these challenges are outlined to guide future research in developing innovative solutions that further optimize and advance the capabilities of SGs.