Electric Power System Management Research Articles

Dynamic stability analysis of the aircraft electrical power system in the more electric aircraft concept

This paper presents the modelling and analysis process of the electrical power management system on board an electrified aircraft conforming to the More/All Electric Aircraft concept. The main objective of the study is to assess the stability of the Electric Power Distribution System under different operational conditions, including the aspect of constant power loads. The paper describes the structure of the system, in which the key role is played by a synchronous generator and a three-phase rectifier converting alternating current AC into direct current DC. To solve the problem, modeling methods in the Matlab/Simulink environment were used, which enabled the analysis of the dynamic properties of the system and the assessment of stability under various types of loads. The obtained test results show that the presence of constant power loads can lead to instability in the EPDS system, manifested by voltage and current oscillations exceeding the standards specified in the MIL-STD-704E standard. The paper also provides tools for predicting system stability without the need to conduct full stability tests, which is important for the design of modern aviation power systems. The final section of the work presents the research results obtained and the resulting significant insights and practical conclusions.

A Study the optimisation of smart grid power flow

Optimal Power Flow (OPF) is a fundamental concept in the management and operation of modern electrical power systems, particularly within the context of smart grids. As the demand for electricity continues to grow and the energy landscape evolves towards greater sustainability, the need for efficient, reliable, and adaptive power systems has become more critical than ever. The primary objective of OPF is to determine the most efficient way to operate the power system while satisfying various technical and operational constraints. This involves the optimization of several variables, including generator outputs, voltage levels, and power flows, to minimize costs, reduce losses, and enhance system reliability. Smart grids can enhance their resilience to disturbances, support the efficient use of renewable energy, and provide reliable electricity to consumers.

Probabilistic short-term load forecasting models based on a parametric approach

The forecasting of electric load plays an essential role in the effective management of electric power systems. Specifically, short-term models, which predict hourly load over the 24 hours of the subsequent day, hold significant value for applications within the realm of electricity markets. In this context, research efforts have predominantly concentrated on the development of point load forecasting models. These models provide solely the estimated value of hourly load, omitting any information regarding its associated uncertainty. Probabilistic forecasting models aim to address this limitation by offering comprehensive information on forecasted values, including their associated uncertainty, thereby enabling their more effective utilization in risky decision-making environments. This paper presents a parametric probabilistic model designed for hourly load forecasting. The model is refined through a multi-objective genetic algorithm optimization process that identifies explanatory variables from a specified set. The selected variables are combined linearly to predict the parameters of a probability distribution function for the hourly load. The process also selects the type of distribution from among those characterized by two parameters. The model is applied to data from a real distribution substation, yielding superior forecasting evaluation indexes compared to those achieved by two benchmark probabilistic load forecasting models.

Unlocking Flexible Electric Vehicle Charging via New Rate Design

A high penetration of electric vehicles (EVs) will deeply impact the management of electric power systems. The risk of not providing adapted EV pricing signals can lead to inefficient investments in grid infrastructure. To avoid costly grid reinforcements and to ensure proper guidance for EV charging, a solution allowing customers to access EV-only rates without installing a separate meter, which we refer to as submetering, is an attractive option for EV owners and grid operators. We develop a game-theoretical model expressed and treated as a mathematical program with equilibrium constraints (MPEC) to capture the interaction between a national regulatory authority (NRA) designing these tariffs and heterogeneous agents. This framework represents a stylized regulatory setup applicable to several European countries. First, we analyse the conditions in which EV-only tariffs can be applied for domestic charging by comparing different energy profiles. Second, we study the impact of EV charging on different tariff structures to identify the most efficient way of recovering network costs. We found that the adoption of submetering under a pure volumetric tariff can bring yearly gains varying from [Formula: see text] to [Formula: see text] to consumers with EV. Finally, we derive policy implications from the results.

Research on the Application of Virtual Reality Technology in the Design and Evaluation of Interactive Interfaces for Renewable Energy Systems

In this investigation, we delve deeply into the visual interaction design of electric vehicle power management systems, particularly how we can integrate sustainable development principles to enhance the user experience and promote environmentally friendly travel. Initially, we take a look at the present situation and challenges faced by electric vehicle power management systems. We identify shortcomings in traditional design approaches that fail to prioritize sustainability, such as low energy efficiency and challenges in accessing user-friendly information. To validate the effectiveness of our proposed method, we conduct case studies and user tests. The results reveal that incorporating sustainable development elements into the visual interaction design not only significantly boosts user satisfaction and trust in the power management system but also helps reduce energy consumption and environmental pollution.

Introduction to the Special Section on Control and Management of Electric Power Systems With High Shares of Inverter-Based Resources

Introduction to the Special Section on Control and Management of Electric Power Systems With High Shares of Inverter-Based Resources

Optimized Load-Scheduling Algorithm for CubeSat's Electric Power System Management Considering Communication Link

Optimized Load-Scheduling Algorithm for CubeSat's Electric Power System Management Considering Communication Link

Electric Car Power Management and Monitoring System: Enhancing Efficiency and Battery Life

Electric Car Power Management and Monitoring System: Enhancing Efficiency and Battery Life

Digital Signal Processor Programming using MATLAB Simulink Embedded Coder

In this work, a computer tool developed in Matlab is presented for process control in real time. The Matlab/Simulink Embedded Code toolbox allows the programming of different commercial acquisition and control cards. In addition, it is easy to supervise the practical cases implemented both from the cards commercial interfaces and from Matlab/Simulink. This work shows how to use the Texas Instruments TMS320F28335 card, the installation of the required software to program the card and the development of a practical case to prove its performance. Thus, the way of generating a sinusoidal voltage waveform using a power DC/AC converter controlled by the PWM (pulse-width modulation) modules card, is presented. This method can be used for teaching and researching purposes, and it is also used for the electric power system management.

Energy-water nexus in low-carbon electric power systems: A simulation-based inexact optimization model

Energy and water resources are closely linked in electric power systems, and the application of low-carbon technologies further affects electricity generation and water consumption in those systems. The holistic optimization of electric power systems, including generation and decarbonization processes, is necessary. Few studies have considered the uncertainty associated with the application of low-carbon technologies in electric power systems optimization from an energy-water nexus perspective. To fill such a gap, this study developed a simulation-based low-carbon energy structure optimization model to address the uncertainty in power systems with low-carbon technologies and generate electricity generation plans. Specifically, LMDI, STIRPAT and grey model were integrated to simulate the carbon emissions from the electric power systems under different socio-economic development levels. Furthermore, a copula-based chance-constrained interval mixed-integer programming model was proposed to quantify the energy-water nexus as the joint violation risk and generate risk-based low-carbon generation schemes. The model was applied to support the management of electric power systems in the Pearl River Delta of China. Results indicate that, the optimized plans could mitigate CO2 emission by up to 37.93% over 15 years. Under all scenarios, more low-carbon power conversion facilities would be established. The application of carbon capture and storage would increase energy and water consumption by up to [0.24, 7.35] × 106 tce and [0.16, 1.12] × 108 m3, respectively. The optimization of the energy structure based on energy-water joint violation risk could reduce the water utilization rate and the carbon emission rate by up to 0.38 m3/104 kWh and 0.04 ton-CO2/104 kWh, respectively.

Parameter Identification of Asynchronous Load Nodes

Asynchronous loads (AL), because of their low negative-sequence resistance, produce the effect of reduced unbalance at their connection points. Therefore, proper modeling of unbalanced load flows in power supply systems requires properly accounting for AL. Adequate models of the induction motor can be realized in the phase frame of reference. The effective use of such models is possible only if accurate data on the parameters of induction motor equivalent circuits for positive and negative sequences are available. Our analysis shows that the techniques used to determine these parameters on the basis of reference data can yield markedly disparate results. It is possible to overcome this difficulty by applying parameter identification methods that use the phase frame of reference. The paper proposes a technique for parameter identification of models of individual induction motors and asynchronous load nodes. The results of computer-aided simulation allow us to conclude that by using parameter identification, we can obtain an equivalent model of an asynchronous load node, and such a model provides high accuracy for both balanced and unbalanced load flow analysis. By varying load flow parameters, we demonstrate that the model proves valid over a wide range of their values. We have proposed a technique for the identification of asynchronous load nodes with such asynchronous loads, including electrical drives equipped with static frequency converters. With the aid of the AL identification models proposed in this paper, it is possible to solve the following practical tasks of management of electric power systems: increasing the accuracy of modeling their operating conditions; making informed decisions when taking measures to reduce unbalance in power grids while accounting for the balancing adjustment effect of AL.

Power Management and Power Quality System Applied in a Single-Phase Nanogrid

This work aims to present an electrical power management system, between a photovoltaic generation system and a battery energy storage system, by using a low-power dual-stage microinverter connected to the electrical distribution network, configuring a nanogrid. The presented system is capable of performing the active power injection of the photovoltaic arrangement, increasing the availability of electrical energy through a maximum power point tracking algorithm. The system is also able to manage the processes of the charge and discharge of the battery system, where the energy storage system will serve as grid support when renewable power is not available. Furthermore, to ensure the quality of electric energy the proposed microinverter also has the characteristic of parallel active filter for suppression of harmonic currents and reactive compensation of local load current, ensuring the quality of electric energy. As a result, this work presents the system topology, modeling and control of electronic converters, the management strategy with its modes of operation and the results of numerical validation of the proposed system.

Online learning for distributed optimal control of an electric vehicle fleet

The management of electrical power systems requires the resolution of large-scale problems whose agents are linked by coupling constraints. Nevertheless, decomposition methods cannot provide an exact solution while dealing with temporal dynamics in a stochastic environment. Indeed, each agent would have to solve a local minimisation in which future quantities intervene. However, these quantities depend on other agents’ future decisions which are still unknown. In order to enhance the existing approximate approaches to this challenge, the proposed method involves Alternating Direction Method of Multipliers to overcome the large dimension by an iterative resolution of local coordinated minimisations. Uncertain temporal dynamics are handled by a stochastic dynamic programming approach. In order to make local problems solvable, the online learning of a Markov process is added. The agents can then anticipate future global variations in a local probabilistic way. The optimal charging of an electric vehicle fleet paired with a wind power plant is considered as a case study. The expected benefits are highlighted, both at the outset and after training the anticipative models. The discussion addresses the learning parameters allowing the fastest convergence.

Design and Build a Water and Electric Power Management System at Public Toilet Using Microcontroller-Based Hybrid Solar Cell

The need for public facilities in densely populated areas is an important matter. One of the needs for public facilities is a public toilet or commonly called MCK. However, the MCK facilities do not yet have management of charging water and electricity. One way to overcome this problem is to use a system that is able to manage water filling and electrical power in the MCK. The problem of insufficient power can be overcome by applying Solar Cell using a Hybrid system so that the existing power becomes more efficient. This study uses a water management system in the toilet bowl using ultrasonic sensors and solenoid valves. The duration of using MCK is known from the PIR sensor. The power management itself uses a hybrid solar cell system where the resources used are batteries and PLN. Then the sensor reading data is sent to firebase and displayed on the website. Based on the test, it was found that the performance of the solar cell during testing on Saturday 10 July 2021 to Monday 12 July 2021 produced an average power of 20.28Watt and the average intensity of sunlight was 680062.32lx. The power used by the load for 3 days is 0.81 kWh. The average usage on the 3 days of the trial was 9.72 Min. Quality of Service testing on a 3-day trial system obtained an average delay of 0.9637308s and packet loss of 2.2%.

Review on Analysis of Power Supply and Demand in Maharashtra State for Load Forecasting Using ANN

The Electric load forecasting (ELF) is a critical procedure in the electrical industry's planning and plays a critical role in electric capacity scheduling and power system management, hence it has piqued academic attention. As a result, for energy generating capacity scheduling and power system management, the accuracy of electric load forecasting is critical. This document provides an overview of power load forecasting methodologies and models. A total of 40 scholarly publications were included in the comparison, which was based on certain criteria such as time frame, inputs, outcomes, project scale, and value. Despite the relative simplicity of all studied models, the regression analysis is still extensively employed and effective for long-term forecasting, according to the research. Machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are preferred for short-term forecasts.

A Distributed Control Approach for Demand Response in Smart Grids

The smart grid is a new concept that has been developed during recent years to improve the intelligence and efficiency of electric power system management. Traditional electricity systems are combined and integrated with information technology, communication technology, and intelligent control technology in the smart grid. Demand Response (DR) refers to the changes in consumers' electricity consumption behavior in response to dynamic pricing or financial incentives. Based on the control manner, DR methods are classified as centralized or distributed. In distributed techniques, customers communicate with the other consumers and provide data to the power utility about the overall use. In this paper, we focus on the distributed approach of DR using the shifting method for a short-term horizon. To be more specific, three well-known solutions were studied: the Resource Allocation with Legitimate Claims, the Constrained Fair-Splitting Dispatch, and Real-Time Pricing. Finally, we compare the different techniques of DR distributed approaches based on the control mechanism.

Research on Power System Material Management Digitalization Technology Based on Process Reengineering

This paper analyzes the problems existing in the material management system of electric power system, probes into the digital technology of material management business of electric power system, and puts forward the application of business process reengineering concept in material management of electric power system. Study the digital method of material management business, use the business process reengineering technology to study the hidden data advantage of the material management business of the city power grid, realize the data flow to the right person automatically and transmit the right data at the right time, solve the contradiction between global demand and scattered supply of data and information.

FMEA of Electric Power Management System for Digital Twin Technology Development of Electric Propulsion Vessels

FMEA of Electric Power Management System for Digital Twin Technology Development of Electric Propulsion Vessels

Морская энергетика на рубеже цифровизации и интеллектуализации системного управления

In the development of the results of the analysis of the directions of development of ship power engineering and automation, promising trends in the development of modern technologies of digitalization and intellectualization of marine equipment are considered. The special role and importance of the methodology and methods of system analysis and research design of complexes and digital technologies of intellectualization of management of complex electric power systems based on monitoring the state and dynamics of their functioning, proactive proactive management of diagnostics and localization of failures, emergencies and accidents is shown. Atypical ways of forced development of new technologies, training of specialists and ensuring competitive superiority of the created ship complexes and systems are given.

Морская энергетика на рубеже цифровизации и интеллектуализации системного управления

In the development of the results of the analysis of the directions of development of ship power engineering and automation, promising trends in the development of modern technologies of digitalization and intellectualization of marine equipment are considered. The special role and importance of the methodology and methods of system analysis and research design of complexes and digital technologies of intellectualization of management of complex electric power systems based on monitoring the state and dynamics of their functioning, proactive proactive management of diagnostics and localization of failures, emergencies and accidents is shown. Atypical ways of forced development of new technologies, training of specialists and ensuring competitive superiority of the created ship complexes and systems are given.