A Station Terminal Management System Based on Scheduling Small Site in Power Dispatching

A Spatial Data Structure for the 3D Graphical Facility Management System

A high proportion of grid-connected renewable energy in power systems represents an economically viable and reliable pathway toward achieving clean and low-carbon power systems. However, the safe and efficient integration of unstable and uncontrollable renewable energy sources remains a significant challenge for power grid operators. Moreover, indiscriminate investment in flexible resources may result in suboptimal resource utilization. Drawing on operational experiences from the power dispatching and controlling center of China Southern Grid (PDCC-CSG), this paper demonstrates how PDCC-CSG mobilizes flexible resources through dispatch mechanisms such as hierarchical coordination, multi-timescale coordination, and “power market plus dispatch” coordination approaches to enable the efficient utilization and consumption of renewable energy in scenarios where new energy accounts for more than 20 % of the total power generation. Furthermore, in order to cope with the scenarios with an even higher share of renewable energy, the future application of advanced cooperative and intelligent dispatch technologies is envisioned.

Objective: The goal of this project is to provide an advanced optimization approach for Dynamic Power Dispatch (DPD) by combining a Unified Power Flow Controller (UPFC) with a Genetic Algorithm (GA). The main objective is to improve power flow management and grid performance in complicated power networks. Methodology: The suggested method takes advantage of the capabilities of the UPFC, a versatile component of the Flexible AC Transmission System (FACTS), and combines them with the optimization power of GA. The UPFC provides close oversight over power flow, voltage ranges, and phase angles, while GA, known for its ability to solve multi-objective and nonlinear problems, improves the UPFC’s installation and operational settings. The effectiveness of this hybrid technique is proven by simulations using the IEEE 14-bus test system. Findings: Simulation results demonstrate that the integration of GA with UPFC yields significant improvements in power flow stability, reduces operational costs and transmission losses, and enhances voltage profiles compared to conventional DPD techniques. Furthermore, the approach increases grid flexibility and stability, addressing the evolving demands of modern power networks. Novelty: This study introduces a unique combination of UPFC and GA to address the challenges in DPD. The approach merges the real-time control capabilities of UPFC with the evolutionary optimization strengths of GA, presenting a scalable and adaptive solution for power dispatch optimization. Applications: The proposed technique is especially useful for improving the efficiency and reliability of current power grids, including those that use renewable energy sources. Its use in smart grid scenarios can help accelerate the transition to more sustainable and resilient energy systems. Keywords: Dynamic power dispatch; Unified Power Flow Controller; Genetic Algorithm; power system optimization; FACTS devices; and an IEEE 14bus testing system

This research proposes a Hybrid Energy System (HES) that integrates solar photovoltaic (PV) panels, wind turbines, and a hydrogen-based storage unit to deliver clean, stable, and sustainable power for a remote off-grid camp. With increasing energy demands in isolated regions and the growing limitations of fossil fuel-based solutions, the need for renewablebased hybrid systems with intelligent energy management is becoming more critical. The proposed system is designed, modeled, and simulated using MATLAB Simulink and Simscape to evaluate performance under varying weather conditions and fluctuating load profiles. In this configuration, the hydrogen storage unit serves as a long-term energy buffer by capturing excess power during periods of high generation and releasing it during low-production intervals. The main objectives of the study are to enhance energy reliability, reduce operational and maintenance costs, and achieve optimal efficiency through intelligent load balancing and power dispatch. Several simulation scenarios are conducted to analyze system response to dynamic load conditions. The results confirm the effectiveness and technical feasibility of the proposed HES in remote areas, offering a sustainable alternative that reduces fossil fuel dependence and ensures long-term energy security.

Transmission lines are crucial for efficient and reliable electricity delivery of electricity but can also be susceptible to faults due to various factors such as external interference, device failures, and ambient conditions. The reliability, accuracy, and efficiency of fault management on the power grid depend heavily on fault classification and fault location. This study integrates a fault detection and classification system for transmission lines and an automatic fault localisation system (ANFIS) for fault location to present a comprehensive approach to managing transmission line problems. The proposed methodology aims to address the technological challenges associated with fault management in power grids by leveraging existing research on radial basis function neural networks (RBFNN) and ANFIS. The integration and validation of the proposed methodology involve incorporating improved fault classification via RBFNN and robust fault locating through ANIS. Simulations and real-world testing will validate the integrated methodology, assessing its performance in various fault scenarios and system configurations. The WSCC 9-bus system is used to validate and test the proposed design, which includes power plants, transformers, transmission networks, and distribution networks. The ClasLoc system has a classification accuracy of 90% and a location accuracy of 89%, ensuring a safe working environment.

ABSTRACTFaults 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.

The voltage source converter (VSC) based multiterminal high voltage direct current (MTDC) transmission system is an interesting technical option to integrate offshore wind farms with the onshore grid due to its unique performance characteristics and reduced power loss via extruded DC cables. In order to enhance the reliability and stability of the MTDC system, an adaptive neuro fuzzy inference system (ANFIS) based coordinated control design has been addressed in this paper. A four terminal VSC-MTDC system which consists of an offshore wind farm and oil platform is implemented in MATLAB/ SimPowerSystems software. The proposed model is tested under different fault scenarios along with the converter outage and simulation results show that the novel coordinated control design has great dynamic stabilities and also the VSC-MTDC system can supply AC voltage of good quality to offshore loads during the disturbances.

This paper proposes the concept of general intelligent dispatch, which is a novel framework of electrical power dispatch based on modern artificial intelligence (AI). The general intelligent dispatch system is based on knowledge automation, parallel system, ACP method, and fully integrated with cutting-edge technologies such as parallel deep learning, parallel dynamics programming. The general intelligent dispatch system is devised to be a comprehensive framework that can support the applications of various modern AI technologies in electrical power dispatch. General intelligent dispatch can accommodate the rapid development of the future smart grid, especially the extensive implementation of distributed energy resources and renewable energy. General intelligent dispatch also provides the practical technical path and framework for the future smart power dispatch services to achieve a higher level of automation and intelligence. Based on the general intelligent dispatch concept, this paper addresses power grid situational awareness, which provides an example for the applications of general intelligent dispatch.

The capability of economic dispatch (ED) algorithms to address the power dispatch problem with unknown cost functions and switching network topology is an important feature for practical applications of power dispatch algorithms in smart grid. Inspired by distributed fast consensus technique and reinforcement learning (RL) approach, this research presents a kind of ED strategy consisting of a fixed-time consensus tracking (FCT) algorithm and a distributed RL-based power dispatch algorithm to address the economic dispatch problem (EDP) with unknown cost functions and switching network topology. Different with existing results on EDP of smart grid where the feasible power outputs are calculated from centralized algorithm, a distributed FCT algorithm is utilised to balance the power demand and output for each dispatch duration, where the achievement of such a consensus leads to feasible power outputs and secures the system performance against switching interaction topology. Then, a distributed RL-based power dispatch algorithm is developed to train a policy for solving EDP with unknown cost functions through the technique of distributed training with distributed execution (DTDE). Finally, case studies are presented to demonstrate the effectiveness of the proposed ED algorithms.

Intermittency of renewable generation entails a large amount of flexibility and spinning reserve to enable secure and stable operation of the power system. The additional imposed cost due to accommodating renewables jeopardizes the economic value of renewable energy projects. Dispatchable renewable generation can help reducing the need for flexibility and spinning reserve. The optimal scheduling unit of an energy management system (EMS) for a dispatchable hybrid renewable power plant is described. It uses forecasting and optimization techniques to optimize the power output from a wind farm, a solar PV system and a battery energy storage system (BESS) such that dispatchable renewable generation is realized. Hence, the hybrid renewable power plant can participate in the interval-ahead electricity market in a similar fashion to conventional units and get dispatched. In addition to reducing the requirement for flexibility and reserve, a dispatchable hybrid renewable power plant can participate in the reserve market using part of the stored energy and any uncommitted power. This is a major leap towards an even higher penetration of renewables in power systems since it directly targets one of the main disadvantages of renewables. A case study is carried out to demonstrate the potential for integrating Sultanate of Oman’s Harweel wind farm with a solar PV system and a BESS to provide dispatchable hybrid renewable generation, with the aid of the introduced optimal scheduling unit of the EMS.

As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

The continuous improvement of the grid voltage level, the application of new energy sources, and the continuous increase of grid load and users have raised higher requirements for power dispatching. And the power dispatching management system(OMS) based on cloud platform is an important tool to ensure the safe and stable operation of the power grid. The platform adopts cloud computing technology to integrate and virtualize resources such as servers, storage, software, and networks, dynamically allocate platform resources through real-time monitoring and reasonable coordination. This paper introduces the key technologies of cloud computing, OMS cloud architecture, cloud platform service architecture and application system, and proposes a cloud computing-based OMS system platform architecture. In addition, it explains the main application modules. The cloud platform architecture will provide strong support for grid dispatching control and management.

In the era of wireless communications, Beyond the 3rd Generation (B3G), a network operator (NO) should satisfy numerous requirements, namely, personalisation, context awareness, always best connectivity, ubiquitous service provision and seamless mobility. A NO can efficiently satisfy the requirements by relying on the different radio networks of its heterogeneous infrastructure, and potentially on other cooperating networks. In this respect, the NO should possess advanced management mechanisms for driving its users to the most appropriate networks that satisfy the requirements. The presentation of such a management system is the specific contribution of this paper. The system is called Reconfigurable Terminal Management System (RTMS). In general, it provides the means for profile modelling, the acquisition of monitoring/discovery/context information, and the negotiation and selection of configurations, based on information deriving from policies, as well as the profiles and the context. Our work focuses on the role and the information of the components of the RTMS. Concrete functionality for accomplishing the role is also presented. Nevertheless, the system is open to the integration of alternate functionality. Our discussion includes a business case that presents in high-level terms the role of the management system, a detailed description of the components of the management system and results that show the efficiency of the management schemes. A summary and further research challenges, conclude this article.

Photovoltaic power prediction under insufficient historical data based on dendrite network and coupled information analysis

The growth of world economic and increasing of trading in most of countries has impact to the number of containers export and import between countries. Some of container terminal is very busy to handle high volume of container movement. Conventional operational procedures have difficulties to handle containers movement then make slow and some issues in terminal operation for container clearance. This paper discus on proposing new algorithm to the current container terminal management system used RFID technology for fast processing and clearance. Container Terminal Management System (CTMS) is a system for port management and interface to the RFID system that used to identify container e-seal, truck and driver identity. Lack of communication and interfacing protocol made slow response during request or reply of message to the gate operator. Proposed algorithm with new procedure of request to CTMS made faster response and avoid inaccuracy of detecting container e-seal. Results of implementation new algorithm have improved to the productivity and efficiency of container terminal. Testing and implementation of this proposed system conducted in a private container terminal in Malaysia.