Smart Power Systems Research Articles

IoT Based Smart Power and Load Management System

IoT Based Smart Power and Load Management System

Smart Power Monitoring System CCTV Menggunakan Internet of Thing

The purpose of this research is to design an intelligent monitoring system to monitor CCTV installed on the highway using internet of things (IOT) technology. CCTV monitoring was previously used only for monitoring and recording events from objects caught by CCTV and can be accessed through an application provided by the supplier of the CCTV brand so that it is not difficult to configure according to the needs of the user. In this research designing CCTV smart power monitoring apart from monitoring and recording objects, this smart power system can find out time, address, code, voltage, electric current, total kwh and electricity costs which display variable values ​​that are measurable and can be controlled through an application that can and accessed using the internet and smartphones. The resulting data can be used as a reference..

Computational social science in smart power systems: Reliability, resilience, and restoration

AbstractSmart grids are typically modelled as cyber–physical power systems, with limited consideration given to the social aspects. Specifically, traditional power system studies tend to overlook the behaviour of stakeholders, such as end‐users. However, the impact of end‐users and their behaviour on power system operation and response to disturbances is significant, particularly with respect to demand response and distributed energy resources. Therefore, it is essential to plan and operate smart grids by taking into account both the technical and social aspects, given the crucial role of active and passive end‐users, as well as the intermittency of renewable energy sources. In order to optimize system efficiency, reliability, and resilience, it is important to consider the level of cooperation, flexibility, and other social features of various stakeholders, including consumers, prosumers, and microgrids. This article aims to address the gaps and challenges associated with modelling social behaviour in power systems, as well as the human‐centred approach for future development and validation of socio‐technical power system models. As the cyber–physical–social system of energy emerges as an important topic, it is imperative to adopt a human‐centred approach in this domain. Considering the significance of computational social science for power system applications, this article proposes a list of research topics that must be addressed to improve the reliability and resilience of power systems in terms of both operation and planning. Solving these problems could have far‐reaching implications for power systems, energy markets, community usage, and energy strategies.

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

Multi-Source Information Fusion Technology and Its Application in Smart Distribution Power System

Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.

Coordinated load frequency control of a smart hybrid power system using the DEMA-TD3 algorithm

In this paper, a novel deep reinforcement learning (DRL) algorithm is proposed for the coordination of controllers in a multi-source hybrid power system (HPS) for load frequency control (LFC). Decentralized controllers regulate the distributed sources with uncertainties in load and source power deviations to maintain the power system frequency. Coordination of decentralized controllers becomes challenging in the presence of dynamic disturbances. A novel LFC framework is introduced via a dynamic environment-based multi-agent twin delayed deep deterministic policy gradient (DEMA-TD3) algorithm to coordinate the decentralized controllers. Moreover, system stability is a concern in systems with renewable integration during islanding operations. To achieve successful islanding, a smart home inverter (SHI) based coordinated control is proposed. Virtual inertia (VI) is added to provide additional frequency support for robust control. Various simulation test scenarios are simulated to analyze the effectiveness of the proposed novel LFC architecture in different modes of operation.

Implementing Optimization Techniques in PSS Design for Multi-Machine Smart Power Systems: A Comparative Study

This study performed a comparative analysis of five new meta-heuristic algorithms specifically adopted based on two general classifications; namely, nature-inspired, which includes artificial eco-system optimization (AEO), African vulture optimization algorithm (AVOA), gorilla troop optimization (GTO), and non-nature-inspired or based on mathematical and physics concepts, which includes gradient-based optimization (GBO) and Runge Kutta optimization (RUN) for optimal tuning of multi-machine power system stabilizers (PSSs). To achieve this aim, the algorithms were applied in the PSS design for a multi-machine smart power system. The PSS design was formulated as an optimization problem, and the eigenvalue-based objective function was adopted to improve the damping of electromechanical modes. The expressed objective function helped to determine the stabilizer parameters and enhanced the dynamic performance of the multi-machine power system. The performance of the algorithms in the PSS’s design was evaluated using the Western System Coordinating Council (WSCC) multi-machine power test system. The results obtained were compared with each other. When compared to nature-inspired algorithms (AEO, AVOA, and GTO), non-nature-inspired algorithms (GBO and RUN) reduced low-frequency oscillations faster by improving the damping of electromechanical modes and providing a better convergence ratio and statistical performance.

IEEE PELS China Membership WIE Subcommittee Co-Sponsors WIP Forum [Society News

On 10 December 2022, the Women in Engineering (WIE) Subcommittee of the IEEE Power Electronics Society (PELS) China Membership Committee co-sponsored the Women in Power (WIP) forum at the 4th International Conference on Smart Power & Internet Energy Systems (SPIES2022). The forum had the theme “Facing the Challenges of Modern Power Systems-Inspiring More Female Engineers” and was held online.

New optimal planning strategy for plug‐in electric vehicles charging stations in a coupled power and transportation network

AbstractThe use of plug‐in electric vehicles (PEV) and their developing technology can create new challenges to the smart power system. The type, method, and time of charging electric vehicles are also other issues. Allocating and determining the optimal capacity of electric vehicle charging stations (EVCS) is related to the technical requirements of the distribution network. This is economically important for the construction of charging stations. This paper proposes a new approach for optimal siting and sizing of PEV charging stations in a coupled electrical and transportation network. This work presents the problem from a techno‐economic point of view of the electrical network as a multi‐objective problem with the objectives of simultaneously reducing the cost of building EVCSs and active power losses. The Pareto method is used to solve the problem and to display optimal points. In order to carry out the simulation, the proposed method is tested on a case study of the standard IEEE 37‐bus network with a 25‐node transport system and the proposed solution in the subject environment. The Floyd–Warshall method is utilized to determine the shortest travel routes for PEVs. The obtained results confirm the effectiveness of the optimal planning of PEV charging stations.

Generalized Method of Mathematical Prototyping of Energy Processes for Digital Twins Development

The use of digital twins in smart power systems at the stages of the life cycle is promising. The dynamics of such systems (smart energy renewable sources, smart energy hydrogen systems, etc.), are determined mainly by the physical and chemical processes occurring inside the systems. The basis for developing digital twins is reliable mathematical models of the systems. In the present paper, the authors present a method of energy processes mathematical prototyping—an overall approach to modeling processes of various physical and chemical natures based on modern non-equilibrium thermodynamics, mechanics, and electrodynamics. Controlled parameters are connected with measured ones by developing a theoretically correct system of process dynamics equations with accuracy up to the experimentally studied properties of substances and processes. Subsequent transformation into particular mathematical models of a specific class of systems makes this approach widely applicable. The properties of substances and processes are given in the form of functional dependencies on the state of the system up to experimentally determined constant coefficients. The authors consider algorithms for identifying the constant coefficients of the functions of substances and processes properties, which complement the proposed unified approach of designing models of various physical and chemical nature systems.

IoT-Enabled Campus Prosumer Microgrid Energy Management, Architecture, Storage Technologies, and Simulation Tools: A Comprehensive Study

Energy is very important in daily life. The smart power system provides an energy management system using various techniques. Among other load types, campus microgrids are very important, and they consume large amounts of energy. Energy management systems in campus prosumer microgrids have been addressed in different works. A comprehensive study of previous works has not reviewed the architecture, tools, and energy storage systems of campus microgrids. In this paper, a survey of campus prosumer microgrids is presented considering their energy management schemes, optimization techniques, architectures, storage types, and design tools. The survey is comprised of one decade of past works for a true analysis. In the optimization techniques, deterministic and metaheuristic methods are reviewed considering their pros and cons. Smart grids are being installed in different campuses all over the world, and these are considered the best alternatives to conventional power systems. However, efficient energy management techniques and tools are required to make these grids more economical and stable.

Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach

AbstractHighly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

Novel secured distributed energy management structure for solar based smart grids incorporating miners

Addressing the blockchain technology (BCT) challenges, such as the high energy consumption is unavoidable in smart power systems to take maximum advantage. As an antithetical point, there is a structural duality between using BCT in the electrical grid due to its advantages and energy consumption. To this end, this research work is an inclination toward controlling the miner’s energy consumption based on a realistic and efficacious two-stage energy mechanism toward the distributed energy concept and optimal placement of data mining devices. In this circumstance, such a new structure allows miners to mainly sustain their hash rates as a prominent index for security achievement and optimally decrease their cost. This paper suggests a novel distributed model for miners coincided with the smart electrical grid getting inspired by the Prima-Dual Method of Multipliers (PDMM) based distributed approach to settle down the needed power bartering between miners and energy supply resources. In addition, for a similar purpose, it seems to reach the ideal resolution needs to perform the modified reaffirmation of miners (the mining pool) within the secured grid. To execute this task, this research recommends the Intelligent Priority Selection (IPS) techniques as well. When all is said and done, the result of this approach can aid researchers with performing in the field of improving social advantages by the usage of the suggested method.

Editorial: Advanced data-driven methods and applications for smart power and energy systems

Editorial: Advanced data-driven methods and applications for smart power and energy systems

AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities

Nowadays, the power grid has become an active colossal resource generation and management system due to the wide use of renewable energy and dynamic workloads processed through intelligent information and communication technologies. Several new operations exist, such as power electrification, intelligent information integration on the physical layer, and complex interconnections in the smart grid. These procedures use data-driven deep learning, big data, and machine learning paradigms to efficiently analyze and control electric power system transient problems and resolve technical issues with robust accuracy and timeliness. Thus, artificial intelligence (AI) has become vital to address and resolving issues related to transient stability assessment (TSA) and control generation. In this paper, we provide a comprehensive review on the role of AI and its sub-procedures in addressing problems in TSA. The workflow of the article includes an AI-based intelligent power system structure along with power system TSA and AI-application rationality to transient situations. Outperforms other reviews, this paper discusses the AI-based TSA framework and design process along with intelligent applications and their analytics in power system transient problems. Moreover, we are not limited to AI, but we also combine the direction of big data that is highly compatible with AI, discusses future trends, opportunities, challenges, and open issues of AI-Big data based transient stability assessment in the smart power grid.

Per-Phase Power Controller for Smooth Islanded Transitions in Three-Phase Three-Wire Systems

This manuscript describes the operation of a droop-based controller for three-phase converters in the case of the absence of a neutral connection to the grid. The controller is capable of output power tracking and smooth transitions into the islanded operation. While independent per-phase control of the converter output power is possible if a neutral connection is present, its absence implies additional constraints to be considered. Focusing on this latter case, the controller described herein allows the independent control of the active power at the output of each phase of the converter and a smooth transition to the islanded operation. These features are paramount in future smart power systems, such as smart microgrids, for implementing demand–response, power-flow management, and uninterrupted power operation.

An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods

The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term load forecasting (STLF) incorporates hour-ahead load forecasting, which is critical for dynamic data-driven smart power system applications. Nevertheless, based on our knowledge, there are not enough academic studies prepared with particular emphasis on this sub-topic, and none of the related studies evaluate STLF forecasting methods in this regard. As such, machine learning (ML) and deep learning (DL) architectures and forecasters have recently been successfully applied to STLF, and are state-of-the-art techniques in the energy forecasting area. Here, hour-ahead load forecasting methods, the majority of which are frequently preferred high-performing up-to-date methods in the literature, were first examined based on different forecasting techniques using two different aggregated-level datasets and observing the effects of these methods on both. Case and comparison studies have been conducted on these high-performing methods before, but there are not many examples studied using data from two different structures. Although the data used in this study were different from each other in terms of the time step, they also had very different and varied features. In addition, feature selection was studied on both datasets and a backward-eliminated exhaustive approach based on the performance of the artificial neural network (ANN) on the validation set was proposed for the development study of the forecasting models. A new DL-based ensemble approach was proposed after examining the results obtained on two separate datasets by applying the feature selection approach to the working forecasting methods, and the numerical results illustrate that it can significantly improve the forecasting performance compared with these up-to-date methods.

On-grid joint energy management and trading in uncertain environment

Consumers in smart power systems may control and manage the energy consumption of their appliances, minimizing peak-to-average power ratios (PAPR), total peak load demands, and their monthly energy expenditures by utilizing the joint energy management and trading framework (JEMTF). The integrated framework of energy management and trading is critical to the integrity of the distribution power system. The role of JEMTF becomes even more challenging when the model is perturbed. This paper discusses the development of a robust optimization technique for solving uncertainty in JEMTF. The JEMTF pattern is perturbed by a number of practical constraints like human interaction factor, load shedding pattern, peak clipping and consumers preferences pattern. The proposed model considers uncertainty in all these constraints in a JEMTF between nearby microgrids, which allows the distribution system to use power at a lower cost. The proposed model focuses on a two-stage system. The first stage is a distribution system that splits into a number of micro-grids by regulating the power demand of each micro-grid using a novel energy management strategy. While, in the second stage, the locally generated energy is exchanged among the micro grids using energy trading mechanisms that meet the needs of customers without placing undue strain on the utility company. The result shows that the suggested strategy lowers the power costs of micro grids by utilizing a high proportion of self-generated electricity intelligently.

Comparison of robust machine-learning and deep-learning models for midterm electrical load forecasting

Electrical load forecasting (ELF) is gaining importance especially due to the severe impact of climate change on electrical energy usage and dynamically evolving smart grid technologies in the last decades. In this regard, medium-term load forecasting, a crucial need for power system planning (generation optimization and outages plan) and operation control, has become prominent in particular. Machine learning and deep learning-based techniques are currently trending approaches in electrical load estimation due to their capability to model complex non-linearity, feature abstraction and high accuracy, especially in the smart power systems environment.
 In this study, several load forecasting models based on machine learning methods which comprise linear regression (LR), decision tree (DT), random forest (RF), gradient boosting, adaBoost, and deep learning techniques such as recurrent neural network (RNN) and long short-term memory (LSTM) are studied for medium-term electrical load demand forecasting at an aggregated level. Performance metric results of these analyzes are presented in detail. State-of-the-art feature selection models are examined on the dataset and their effects on these forecasting methods are evaluated. Numerical results show that forecasting performance can be significantly improved. These results are validated by the results of other studies on the subject and found to be superior.

Microgrid to smart grid's evolution: Technical challenges, current solutions, and future scopes

AbstractRenewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation.