Grid Management Research Articles

Demand side management using ant colony optimization algorithm in renewable energy integrated smart grid

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.

Leveraging IoT for Smart Grids and Energy Management in Electrical Systems: Applications, Benefits, and Challenges

The integration of the Internet of Things (IoT) into electrical systems is transforming traditional power grids into intelligent, data-driven networks, capable of real-time monitoring, control, and optimization. This transformation, often referred to as the development of “smart grids,” presents opportunities for improving energy efficiency, enhancing reliability, and ensuring sustainability. IoT facilitates dynamic demand response, predictive maintenance, and seamless integration of renewable energy sources, positioning it as a key enabler of the energy transition. This paper provides a comprehensive overview of the role of IoT in electrical systems, with an emphasis on smart grids, predictive maintenance, and energy management in smart buildings. It explores IoT’s architecture, benefits, and challenges, including cybersecurity concerns and the lack of industry-wide standardization. The study also incorporates real-world data and graphical representations to illustrate the impact of IoT in optimizing power systems. Additionally, this paper discusses future prospects for IoT in electrical systems and suggests solutions to overcome existing challenges.

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

A BPSO based collaborative management platform for multi-source data security in power grids

By designing a digital power grid multi-source data security collaborative management platform, the system configuration problem of the OMS system and the power grid management platform for the main distribution network of the power grid is solved. A design method for the digital power grid multi-source data security collaborative management platform based on discrete particle swarm optimization algorithm is proposed. Based on the design concept of SOA, realize the overall design framework of the platform according to the design method of multi-layer technical system in the business presentation layer, business process and composition layer, service layer, component layer and resource layer, realize the basic layer design of the system management platform through the basic application platform design scheme of XML configuration, implement the query, processing and output representation of the grid’s multivariate data using B/S architecture protocol, and use the Spring Framework The platform software architecture is implemented using J2EE technology and multi module component design scheme. The discrete particle swarm optimization algorithm is used for the fusion and scheduling of multi-source data in the digital power grid. The interface design and functional construction of the power grid management platform are implemented in the OMS system of the power grid main distribution network, and the logical model of the transformation project is constructed to achieve platform optimization and construction. Tests have shown that the designed digital power grid multi-source data security collaborative management platform has good human-machine interaction, strong data fusion scheduling ability, reduced resource and subsystem coupling, and supports the flexibility of physical deployment and maintenance.

Empowering the Future : The Vision of Smart Transmission Grids

In the quest for an adaptable, dependable, and sustainable energy supply, the modern power grid necessitates a transformative upgrade towards intelligence. While substantial efforts have been devoted to envisioning and advancing smart power grids in the United States and Europe, the focus has predominantly centered on the distribution grid and demand-side management, leaving the holistic perspective of the transmission grid within the context of smart grids somewhat obscured. This paper proposes a distinct vision for the future of smart transmission grids, delineating their pivotal attributes. In this envisioned framework, each smart transmission grid emerges as a cohesive, integrated system comprising three interrelated smart components: smart control centers, smart transmission networks, and smart substations. The paper meticulously outlines the characteristics and functionalities of each of these three essential components, elucidating the enabling technologies necessary to realize their envisioned capabilities. By conceptualizing smart transmission grids as multifaceted systems driven by intelligent components, this vision underscores the potential for enhancing grid efficiency, reliability, and sustainability. Through the seamless integration of advanced control centers, transmission networks, and substations empowered by cutting-edge technologies, smart transmission grids aim to optimize grid operations, facilitate dynamic energy management, and foster resilience against emerging challenges and disruptions.

Application of Big Data Analysis and Intelligent Algorithm in Power System Operation Optimization

The power communication system provides powerful technical support for realizing the intelligent operation and information management of the power grid and improving the operation efficiency and power supply quality of the power grid. Quantum key distribution (QKD) is considered one of the most promising technologies for commercialization. QKD uses a single photon to encrypt data to produce a more secure and reliable password. This paper intends to study the hierarchical, centralized control architecture of power dispatching based on quantum essential supply (QKD). The performance indexes of MDI-QKD under symmetric and asymmetric conditions were studied by local optimization. The optimal key formation rate of the algorithm is analyzed. From the perspective of quantum critical utilization, a quantum key utilization scheme for grid backbone dispatching service is proposed. The dynamic adjustment test of multi-node time slot and service key update rate is carried out. Experiments show that the MDI scheme can effectively improve the effectiveness of a multi-node QKD system. Thus, the security of data transmission of the core business of power dispatching data networks can be ensured to the greatest extent. AMDI can effectively reduce the transmission timeout of low-priority data streams because the delay of high-priority data streams reaches the proportion. It can be an excellent solution to the power system and the password requirements.

The Construction and application of analytical framework of power grid management system

The power grid management system is an important institutional foundation supporting the development of the electric power industry. The current power grid management system analysis framework with the relationship between transmission and distribution as the core cannot explain the facts well. This paper builds a power grid management system analysis framework composed of "structure layer + factor layer", which can analyze the basic outline of a country's power grid management system and the driving factors behind it in a more comprehensive and systematic way.

Distributed Energy Resource Integration for Carbon Neutral Power Systems: Market‐Based Approaches to Ancillary Services and Microgrid Operation

Electricity, as a sustainable energy carrier, plays a central role in the transition scenarios for carbon neutralization of energy systems. Expanding the potential of electricity requires intelligent integration of electricity infrastructures and electricity markets with distributed energy resources (DERs) including roof‐top solar photovoltaics (PVs), controllable loads, and storage batteries. This integration will advance the management of power distribution grids, resulting in harmonization of the distribution grids and variable renewable generation. In the foreseeable future, the DERs will be actively used to perform advanced management of power distribution grids and compensate fluctuation in variable renewable power generation such as PVs and wind power. Market‐based coordination mechanisms are already practiced in large‐scale electricity production, electricity trading and transmission around the world. Using these mechanisms at the distribution grid level to integrate numerous flexible devices into the grids remains an important research topic. This paper reviews the role of DER integration toward carbon‐neutral power systems, the state‐of‐art and challenges associated with market‐based approaches for integrating flexible demand, distributed generation, and storage. Methods for assessing the value of DER aggregation such as virtual power plants and microgrids are also explored. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

The Importance of AI-Enabled Internet of everything Services for Smart Home Management

Abstract Smart home applications are ubiquitous and have become popular because of the overwhelming use of the Internet of Things (IoT) and artificial intelligence (AI). Living smart with automation and integrated AI-IoT has become more affordable as home automation technologies have matured. In addition, the Internet of Everything (IoE), which involves the interconnection of humans, businesses, and intelligent objects, has the potential to reshape various industries. However, the rising energy cost and demand have led numerous organizations to determine smart ways to monitor, control, and save energy. Hence, this study suggests AI-Enabled Internet of Everything Services (AI-IoES) for efficient smart home energy management. The data have been taken from the Open Smart Home IoT//Energy Dataset for analyzing the energy consumption of home appliances. This paper presents an IoT sensor for energy management to track and control specific loads in smart homes. The deep neural network (DNN) is built for secure demand-side management (DSM) in an IoT-assisted smart grid and trained on the extracted feature from electricity consumption information gathered using an IoT sensor. The system is established with real-time monitoring and a user interface for remote control and access. The experimental outcome demonstrates that the suggested AI-IoES system increases the user experience by 98.9%, energy efficiency ratio (EER) by 97.8%, and accuracy ratio by 97.2%, and reduces energy consumption by 19.2% compared with other existing methods.

Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption

ABSTRACT Smart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

Effectiveness of grid management in emergency operating room for cancer patients

Effectiveness of grid management in emergency operating room for cancer patients

All sky imaging-based short-term solar irradiance forecasting with Long Short-Term Memory networks

The intermittent nature of solar irradiance, primarily due to cloud movements, leads to rapid short-term fluctuations in the power output of photovoltaic (PV) systems. These fluctuations pose a significant challenge for integrating this renewable energy source into the power grid. Accurate forecasting of solar irradiance is not only crucial but also multi-beneficial. It enables more precise grid management by allowing operators to anticipate power output fluctuations and adjust energy distribution and storage strategies accordingly. This proactive approach reduces the reliance on backup power sources, which are often less sustainable and more expensive. Furthermore, accurate forecasts enhance the overall efficiency and reliability of energy systems by minimizing the impact of power variability on the grid, thereby supporting a more stable and sustainable energy supply.Addressing this need, our study focuses on the development of a forecasting model through innovative feature engineering, systematic design of specific attributes, and optimization of sequence length. The model is tailored to perform efficiently across various weather conditions and offers predictions for a time horizon of 0 to 20 min ahead. Utilizing a Long Short-Term Memory (LSTM) model, we achieve a remarkable ramp Forecast Skill Score of 39% in sunny and 25% in partially cloudy conditions. This work not only contributes to the existing literature but also presents a pioneering methodology for solar energy integration, highlighting the importance and application of accurate short-term solar irradiance forecasting.

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

The new situation and optimization strategy of China's power grid management system

The system mechanism of power grid management is an important institutional basis to support the development of electric power industry, and has always been an important controversial issue in China's electric power system reform. Based on the current situation of China's power grid management system, this paper analyzes the new situation, analyzes the main problems, and finally, based on the background of new power system construction, systematically puts forward the overall thinking and specific strategies of China's power grid management system optimization.

Power line Communication: Revolutionizing data transfer over electrical distribution networks

Power Line Communication (PLC) is an emerging technology that utilizes existing electrical power infrastructure for data transmission. It enables communication over power lines, allowing devices to exchange information and access the internet through the power grid. This method offers numerous advantages, including widespread availability, cost-effectiveness, and easy deployment without the need for additional cabling. PLC has found applications in various domains, including smart grid management, home automation, industrial control systems, and Internet of Things (IoT) connectivity. This abstract provides an overview of PLC, highlighting its benefits, challenges, and key technologies. It also discusses the potential of PLC in transforming power distribution networks into intelligent and interconnected systems. Additionally, emerging trends and future directions for PLC research and development are explored, emphasizing the potential for enhanced reliability, speed, and efficiency in data transmission over power lines. This paper proposed a two way communication from source to load sides using BPSK and QPSK modulation techniques. Monte Carlo simulation is used to predict the transformer theoretical channel (AWGN) and compare the efficiency of the proposed methods. Randomly selected data from 10 k to 50 k size are used to compare the performance curve. Moreover, a test sound data is sent from the source side to the load side and it is observed that using the QPSK modulation technique has almost zero Bit Error Rate (BER).

Revolutionizing Power Systems Through Electrical Automation: Efficiency, Reliability, and Sustainability

This paper explores the transformative role of electrical automation technologies in the modernization and optimization of power systems. With a comprehensive analysis spanning power generation, distribution, and grid management, we delineate the multifaceted impacts of automation on enhancing operational efficiency, ensuring reliability, and promoting environmental sustainability. Through the lens of operational case studies and theoretical frameworks, we identify key insights into how electrical automation contributes to the streamlining of processes, reduction of human error, and integration of renewable energy sources. Moreover, we delve into the future trajectory of electrical automation, highlighting the potential integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms, the expansion of smart grid capabilities, and the paramount importance of cybersecurity in the digital era of power systems. Our discussion synthesizes these perspectives to underscore the pivotal role of electrical automation in driving the evolution of power systems towards greater adaptability, efficiency, and resilience. This paper posits that the future of power systems hinges on the strategic implementation of advanced automation technologies, guided by an overarching commitment to innovation, security, and sustainability.

End-year China wind power installation rush reduces electric system reliability

The urgent challenge posed by climate change has catalyzed global efforts to transition towards sustainable energy sources, with wind power emerging as a pivotal component. However, the rapid expansion of renewable energy sources has raised concerns about electric system reliability, given their intermittent and hard-to-forecast nature. China has provided incentives that promoted the rapid expansion of wind. However, the structure of some incentives led to the phenomenon of end-year rushes to install wind power before incentives expire. Leveraging panel data from China's provinces, we empirically estimate the impact of these installation rushes on electric reliability. We find significant adverse effects, with a one-standard-deviation increase in installation rush corresponding to a 0.767% decrease in the reliability rate and a 39.6-min increase in annual outage duration. Notably, urban areas and the northwestern grid are particularly vulnerable to the disruptions caused by year-end installation rushes. In the urban areas of the northwestern grid, we identify the potential for substantial improvements in the lower bound of the reliability rate, from 98.86% to 99.37%, or a reduction in outage duration from 11.65 h to 7.16 h. These findings show the importance of structuring incentives properly and the importance of improvements in grid infrastructure and management in the transition to a low-carbon world.

Renewable energy management in smart grid with cloud security analysis using multi agent machine learning model

Machine learning makes it possible for challenging jobs to be finished totally on their own. Computers as well as mobile devices may make it simpler to regulate interior temperature, keep an eye on security, and carry out periodic maintenance in a smart grid (SG). The smart grids' capacity to detect cyber attacks is crucial for determining the availability and dependability of the operation. Integrity of bogus data cyber attacks in physical layers of smart grids (SGs) is highlighted in this article. This study suggests a unique method for managing the energy usage of smart grids while analysing the security of data transfer. Here, the energy efficiency of the smart grid network is evaluated using renewable energy sources (SM_RES), and the network has been watched for security evaluation in cloud computing. Multi agent Markov reinforcement learning (MAMRL) is used to analyse the security of the cloud network in the smart grid. In terms of scalability, QoS, energy efficiency, power consumption, prediction accuracy, RMSE, average precision, and data integrity, experimental study is conducted for energy management and network security. Proposed technique attained prediction accuracy 95 %, packet loss rate 77 %, RMSE 88 %, Average precision 85 %, network security analysis 97 %; scalability 95.8 %, QoS 86 %, Power consumption 35 % and energy efficiency 96 %.

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

A hybrid deep learning model with an optimal strategy based on improved VMD and transformer for short-term photovoltaic power forecasting

The precise forecasting of photovoltaic (PV) power is important for efficient grid management. To enhance the analysis and processing capability of PV characteristics, address the feature extraction challenges for long sequences, and improve forecasting accuracy, this study presents a robust hybrid deep learning model for PV power forecasting. First, a dynamic mean pre-processing algorithm is applied for data cleaning. Subsequently, an improved whale variational mode decomposition (IWVMD) algorithm is proposed for data decomposition in multichannel multi-scale modeling. Furthermore, a novel context-embedded causal convolutional Transformer (CCTrans) structure is used to predict each subsequence, and an optimal strategy is formulated for both input and output under the combined dynamic contextual information and single target variable forecasting (CDCTF) pattern. Finally, the forecasting results are reconstructed. Experiments are conducted to evaluate the performance of the model across different seasons, using publicly available datasets from the Desert Knowledge Australia Solar Center (DKASC). Ablation studies, validation with diverse datasets, and comparisons with other models confirm the effectiveness, accuracy, robustness, and generalizability of the model. In addition, recommendations for optimal forecasting ranges for different seasons are provided.