Smart Power Distribution System Research Articles

A hybrid approach for power quality event identification in power systems: Elasticnet Regression decomposition and optimized probabilistic neural networks

The transformation of traditional grid networks towards smart-grid and microgrid concepts raises many critical issues, and quality in the power supply is one of the prominent ones that needs further research. Developing and applying power quality (PQ) recognition methods with efficient and reliable analysis are essential to the fast-growing issues related to modern smart power distribution systems. In this regard, a hybrid algorithm is proposed for PQ events detection and classification using Elasticnet Regression-based Variational Mode Decomposition (ER-VMD) and Salp Swarm Algorithm optimized Probabilistic Neural Network (SSA-PNN). The Elasticnet Regression (ER) process is suggested to modify the conventional VMD approach instead of the Tikhonov Regularization (TR) method to enhance performance and obtain better band-limited intrinsic mode functions. This idea results in robust and effective reconstruction features and helps to obtain accurate classification using the classifier. In the classification stage, a Salp Swarm Algorithm (SSA) based PNN is used for the PQ event, considering the relevant features obtained from ER-VMD. The system parameters often influence PNN performance, and SSA is used to determine the ideal values to improve the PNN's capacity for more accurate classification. The numerical values of the accuracy percentage, percentage of sensitivity, and percentage of specificity in the case of real-time data are found as 98.58, 100, and 98.46, respectively. The acquired comparison findings demonstrate the effectiveness and robustness of the proposed technique in terms of rapid learning speed, smaller computational complexity, robust performance for anti-noise conditions, and accurate identification and categorization.

Studying the Intelligent Power Distribution and Consumption of the Power Grid and its Cost Structure Using Big Data Analysis

Based on the analysis of big data, this paper studies the impact of user behavior response on the cost structure of the microgrid system in the power grid system. The article first conducts in-depth research on distributed power generation and energy storage systems, focusing on the principles and output characteristics of smart power distribution and utilization in power grids, researches smart power distribution and utilization systems in power grids, and makes a more comprehensive discussion of the current situation. Secondly, a big data analysis platform was built, and distributed storage and computing were studied. The platform was used to perform distributed storage and regulation of electricity consumption data, and the electricity consumption information data was divided into the important load, controllable load and transferable load, constructed a microgrid system model based on electricity consumption behavior response, and analyzed a calculation example. After that, a micro-grid system was simulated based on HOMER software, and the optimal capacity configuration of the system was performed. Under this configuration, the micro-grid system has the highest economic efficiency. At the same time, a demand-side load control system was built. Introduced distributed power as a controllable load, integrated new energy access and load control technology, coordinated the contradiction between the power grid and distributed power, and completed a cost-benefit analysis. Finally, for demand-side management electricity price response, peak-valley time-of-use price, the most important implementation method, according to the cost structure theory, the peak-valley period is divided according to the membership function in fuzzy mathematics, and the user's response model to peak-valley time-of-use price is established. The experiment uses the original data to simulate, find the user response model parameters based on the load transfer rate, and complete the comparative analysis of the effect under the peak-valley time-of-use electricity price, which is of great significance to the implementation and improvement of the peak-valley time-of-use electricity price project. The analysis results of the calculation examples show that the method constructed in this paper can effectively realize the power quality analysis of the distribution network in the big data environment. The research results provide technical support for the management of the rural grid voltage deviation of the power company, and lay the foundation for improving the safe operation and management of the power grid.

Resilience Development Approach for Smart Power Distribution System Coupled with Smart Green Transportation System on Cloud Computing Platform

Resilience Development Approach for Smart Power Distribution System Coupled with Smart Green Transportation System on Cloud Computing Platform

Deep learning anomaly detection in AI-powered intelligent power distribution systems

Introduction: Intelligent power distribution systems are vital in the modern power industry, tasked with managing power distribution efficiently. These systems, however, encounter challenges in anomaly detection, hampered by the complexity of data and limitations in model generalization.Methods: This study developed a Transformer-GAN model that combines Transformer architectures with GAN technology, efficiently processing complex data and enhancing anomaly detection. This model’s self-attention and generative capabilities allow for superior adaptability and robustness against dynamic data patterns and unknown anomalies.Results: The Transformer-GAN model demonstrated remarkable efficacy across multiple datasets, significantly outperforming traditional anomaly detection methods. Key highlights include achieving up to 95.18% accuracy and notably high recall and F1 scores across diverse power distribution scenarios. Its exceptional performance is further underscored by achieving the highest AUC of 96.64%, evidencing its superior ability to discern between normal and anomalous patterns, thereby reinforcing the model’s advantage in enhancing the security and stability of smart power systems.Discussion: The success of the Transformer-GAN model not only boosts the stability and security of smart power distribution systems but also finds potential applications in industrial automation and the Internet of Things. This research signifies a pivotal step in integrating artificial intelligence into the power sector, promising to advance the reliability and intelligent evolution of future power systems.

A co-optimized approach for state estimation based micro phasor measurement unit allocation in power distribution infrastructure

Due to the highly complex and dynamic electrical characteristics of the 3-phase unbalanced power delivery networks (PDNs), it is challenging for utilities to observe the network entirely. This impediment necessitates the PDNs to become intelligent nowadays in the presence of various smart devices. Micro-phasor measurement units (µPMUs) are expected to play a crucial role in observing the smart power distribution system due to their micro-seconds resolution and millidegree precision accuracy. The placement of µPMUs in PDNs is also very challenging due to their associated costs. So, allocating a minimal number of µPMUs in PDN with fulfilling the topological observability can elucidate the issue of real-time visibility of the entire system. This paper presents a novel co-optimized placement strategy of µPMUs (CPMP) in PDNs that minimizes the µPMU numbers and the estimation error in the buses other than the µPMU assigned nodes. This work also displays a novel dynamic heuristic distribution system state estimation (DDSSE) to estimate the system's unknown states by using only the information of bus voltage phasors at the µPMU specified buses. The DDSSE is used to estimate the root mean square error (RMSE) in estimated voltage magnitudes and angles. Besides this, the efficacy of the grey-wolf optimizer (GWO) is presented in this work to solve the newly formulated placement problem. The results are validated on an Indian 19-bus and 34-bus unbalanced radial distribution system (URDS), and a detailed comparative study of different heuristic algorithms is also analyzed in this work.

Real-time detection of stealthy IoT-based cyber-attacks on power distribution systems: A novel anomaly prediction approach

IoT-enabled appliances are essential components in the development of modern smart homes and cities as they offer energy-efficient solutions that bring comfort to customers, resulting in a better quality of life. Despite the advantages, these smart appliances also present new privacy and security challenges that create vulnerabilities in both smart homes and Power Distribution Systems (PDSs), ultimately affecting their efficiency and reliability. Hence, identifying the security risks linked with behind-the-meter (BTM) IoT-enabled appliances is crucial to develop appropriate countermeasures for safeguarding the PDSs. Previous studies have primarily focused on developing countermeasures for attacks on utility-scale domains, while the problem of attack detection has been largely neglected in the literature. This paper fills the gap by presenting a stealthy attack model applied to a group of compromised IoT-enabled appliances manipulating their operation to deteriorate the power quality of a PDS. The presented attack minimizes the chance of getting detected by maintaining the operating conditions of such appliances within the consumers’ comfort levels. Then, a novel real-time anomaly detection and prediction method is proposed based on demand power spectrum analysis and Long-short-term memory (LSTM) neural networks. Further, a Feeder Loading Abnormal Power Spectrum (FLAPS) index is introduced and utilized to estimate the attack onset time, which is then reported to the PDS’s operator for further investigation and action. The evaluation of the stealthy attack model and the anomaly prediction approach is carried out on a residential community modeled using the IEEE-13 bus power distribution feeder, which exhibits various power consumption patterns and appliance operation schedules. The effects of the proposed stealthy attack on the PDS are analyzed quantitatively by examining the occurrences of voltage violations and the wear and tear of the line voltage regulator (LVR) of the system. Numerical results show that compromising only 5% of the PDS’s load, the LVR operates 1.8 more times than during normal conditions. This impact increases to 8 times at 50% compromised load with many voltage violation cases. Also, the simulation results demonstrate the superiority of the proposed prediction technique to detect stealthy attacks 50% faster than threshold-based approaches, thereby enabling the system’s operator to take faster actions.

Guest Editorial: Special issue on application of new generation information technology in the smart power distribution and utilization system

Guest Editorial: Special issue on application of new generation information technology in the smart power distribution and utilization system

Multi-Stage Resilience Management of Smart Power Distribution Systems: A Stochastic Robust Optimization Model

Significant outages from weather and climate extremes have highlighted the critical need for resilience-centered risk management of the grid. This paper proposes a multi-stage stochastic robust optimization (SRO) model that advances the existing planning frameworks on two main fronts. First, it captures interactions of operational measures with hardening decisions. Second, it properly treats the multitude of uncertainties in planning. The SRO model coordinates hardening and system operational measures for smart power distribution systems equipped with distributed generation units and switches. To capture the uncertainty in the incurred damage by extreme events, an uncertainty set is developed by integrating probabilistic information of hurricanes with the performance of overhead structures. A novel probabilistic model for the repair time of damaged lines is derived to account for the uncertainty in the recovery process. A solution strategy based on the integration of a differential evolution algorithm and a mixed-integer solver is designed to solve the resilience maximization model. The proposed approach is applied to a modified IEEE 33-bus system with 485 utility poles and a 118-bus system with 1841 poles. The systems are mapped on the Harris County, TX, U.S. Results reveal that optimal hardening decisions can be significantly influenced by resilience operational measures.

Experimental analysis of fractional order PI λ D μ controller for improvement of power quality in smart grid environment

Power electronics load is considered to be a strengthening factor that leads the system operators more concerned about power quality issues. Poor power quality can lead to distinctive operation of electrical components and devices, which may cause heavy economic loss to the customers and power network operators. An application of fractional order PIλDμ (FOPIλDμ) controller for power quality improvement in smart power distribution systems is presented here. Analytical design steps are presented for the FOPIλDμ controller. A genetic algorithm is used for tuning the control parameters of the FOPIλDμ controller optimally. The FOPIλDμ controller is employed and evaluated for shunt active power filter (APF) to filter out harmonics and improve reactive power compensation for non-stationary, non-linear load conditions for a smart power distribution system. Effectiveness of FOPIλDμ controller for instantaneous reactive power theory based PQ control algorithm applied to shunt APF is validated using a small-scale laboratory experimental setup. The experimental results confirm the excellent performance of the FOPIλDμ controller in terms of its superior transient response, significant reduction in harmonic distortion, and improved reactive power compensation in a smart grid environment.

Trilevel smart meter hardening strategy for mitigating cyber attacks against Volt/VAR optimization in smart power distribution systems

Smart meter data are crucial information for applications in distribution management system (DMS) to manage the distribution system in a reliable and secure manner. However, the smart meter can be attacked by an adversary through the injection of false data into smart meters. This cyber attack can lead to malfunction of the applications that use smart meter data, thereby yielding an abnormal distribution system operation. This paper presents a multilevel optimization-based smart meter hardening strategy where a defender can mitigate the detrimental impact of cyber attacks on volt/VAR optimization (VVO) in DMS via the manipulation of smart meter data. A trilevel defender–attacker–defender (DAD) model with limited attack and defense resources is proposed to determine an optimal attack mitigation strategy. The proposed DAD model aims to identify smart meters that must be hardened from the VVO attack (upper level) while considering both the worst-case attack against the VVO (middle level) and the VVO during the attack (lower level). A nested column-and-constraint generation algorithm is applied to the DAD model to identify and prioritize the most essential smart meters that should be hardened to mitigate the impact of the VVO attack. An IEEE 33-node distribution feeder was used to quantify the performance of the proposed mitigation framework in terms of voltage deviation, solar photovoltaic (PV) penetration level, attack/defense resources, and computational complexity. The simulation results show that mean percentage errors for voltage deviation of the attack and the defense are calculated as 22.02% and 6.22%, respectively.

Development of methodological foundations for the development of energy in Industry 4.0 in part of game theory and blockchain

The object of research is the implementation of the principles of Industry 4.0, in particular game theory and blockchain, for the development of energy. One of the obstacles to development is the problem of energy supply in modern economic conditions in terms of achieving sustainable development goals. In the course of the research, a systematic approach to the critical analysis of scientific sources of information related to the development of the energy sector, in particular renewable energy, was used. The scientific result of the systemic interaction of distributed generating devices with «smart» consumers using the principles of Industry 4.0 has been obtained. These principles make it possible to increase the level of efficiency of the power system, to reduce the total cost of operating the power system. And also to reduce power losses, improve network performance while reducing CO2 emissions and negative impact on the environment. The possibility of implementing the provisions of Industry 4.0 as a prerequisite for an economical supply of energy is given. The research methodology is proposed based on a critical analysis of the sphere of economics and management in the energy sector in the period 2011–2021. The possibility of using the Vehicle-to-Grid concept based on Industry 4.0 as a component of the energy system is considered. Due to this, a method for managing the sale of electricity is proposed, based on game theory, and taking into account the autonomous response to demand and the interaction of distributed generation in smart power distribution systems using non-cooperative games. The authors have identified the possibilities of energy blockchain technology in terms of efficient energy supply and transformation of commercial relations in the energy market. Focuses on blockchain for energy as part of ensuring energy sustainability, providing opportunities for the development of renewable energy sources. A list of breakthrough energy technologies for the last 5 years has been highlighted with the aim of their implementation to improve the levels of quality and safety of human life. In particular, according to the Massachusetts Institute of Technology, there are 7 technologies out of 50 investigated, directly related to energy, moreover, the overwhelming majority of these technologies are related to renewable energy.

Fuzzy logic-based management of hybrid distribution transformer using LoRa technology

Hybrid distribution transformers (HDT) has a better performance than traditional distribution transformers on improving power quality, such as reducing harmonics, unbalance, voltage fluctuations and low power factor in the future smart power distribution system. In order to increase the service life and reliability of hybrid distribution transformers, this paper proposed a remote management system using LoRa technology based on fuzzy logic. HDT based on fuzzy logic judgment system (FLJS) replaces the Boolean logic with fuzzy logic and several power quality problems including power factor, load-side current harmonics and voltage unbalance are considered, as well as grid-side voltage deviation and unbalance. The management system can dynamically adjust the working states of HDT according to the output results of the FLJS to reduce the use time of power electronic devices. Due to the application of LoRa, the management system can remotely adjust the parameters of the FLJS in real time for different distribution network nodes to avoid frequent switching of HDT working states. Meanwhile, it is able to remotely monitor the real-time working states and fault states of HDT to reduce recovery time and maintenance costs in case of HDT failure. Finally, simulation and experimental results are provided to verify the effectiveness of the proposed management system for HDT.

A novel complex linear state estimator for smart power distribution systems: methodology and implementation

The electric power distribution systems become increasingly complex due to the high penetration of renewable energy sources, which gradually lead to more challenges or even risks to the grid operation. The need to enhance monitoring and control of the power distribution systems is in great demand. Phasor measurement units (PMUs) have been widely deployed in transmission networks to provide better situational awareness of the system. The same philosophy can be applied to power distribution systems by using the distribution level PMUs (D-PMUs). This paper proposes a novel complex linear state estimator considering parameter noise (CLSE-PN) using D-PMU data for power distribution systems. An implementation of the system architecture is also proposed to accommodate the CLSE-PN algorithm. Case studies demonstrate the effectiveness of the algorithm and prove that CLSE-PN provides more accurate results than the CLSE which does not consider parameter noise.

Stability of Transactive Energy Market-Based Power Distribution System Under Data Integrity Attack

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.

Power quality improvement of smart microgrids using EMS-based fuzzy controlled UPQC

The prevalent power quality problems in smart microgrids and power distribution systems are voltage sag, voltage swell, and harmonic distortion. The achievement of pure sinusoidal waveform with proper magnitude and phase is currently a great research and development concern. The aim of this paper is to evaluate and mitigate the smart microgrid harmonics, voltage sag, and voltage swell throughout a 24-h cycle, taking into consideration the variation in solar power generation due to changes in irradiation received by photovoltaic cells, the variation in wind power generation due to changes in wind speed, and the variation of linear and nonlinear load profiles during the day’s cycle. The mitigation of the power quality issues manifested in current harmonics, voltage sag, and voltage swell is achieved through the implementation of a new fully fuzzy controlled unified power quality conditioner (UPQC). It is controlled by an energy management system (EMS). This paper introduces a new control system for the UPQC using full fuzzy logic control. Moreover, fuzzy control is used in current control instead of proportional integral controllers so that it has acceptable performance over a wide range of operating points. The novel approach of an EMS-connected UPQC activates the UPQC at the required time only into the grid. This approach has many benefits by increasing the UPQC lifetime. The effect of the proposed system on the aforementioned issues has been validated through simulation by MATLAB/Simulink. The results are compared with those obtained by conventional methods. The results verify the superior performance of the proposed UPQC to mitigate the problems of current total harmonic distortions, voltage sag, and voltage swell under different operating conditions during the monitoring period.

An Aggregated Model for Energy Management Considering Crowdsourcing Behaviors of Distributed Energy Resources

Increasing deployment of distributed energy resources (DERs) is re-sculpturing the modern power systems in recent years. Future smart power distribution systems should be competent at accommodating extensive integration of DERs and managing the associated uncertainties at the distribution level. The electricity market has been proved to be an efficient way to employ market signals to direct behaviors of users and DERs with large capacity and homogeneous pattern. However, existing market frameworks cannot effectively handle a large number of small-scale DERs due to their diverse characteristics and arbitrary behavior patterns. In this context, an aggregated model which can represent and manage a diverse collection of DER, load, and storage is proposed. An additional trading platform, namely the energy sharing market, is established to reinforce the coordination and collaboration among various aggregators as well as operators. Energy sharing scheme is applied and a corresponding dynamic dispatch platform is designed to solve the crowdsource problem. The efficiency of the proposed model is validated by the numerical studies, and the market performance and impacts of energy sharing on the power systems are illustrated.

A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost

The estimation of losses of distribution feeders plays a crucial guiding role for the planning, design, and operation of a distribution system. This paper proposes a novel estimation method of statistical line loss of distribution feeders using the feeder cluster technique and modified eXtreme Gradient Boosting (XGBoost) algorithm that is based on the characteristic data of feeders that are collected in the smart power distribution and utilization system. In order to enhance the applicability and accuracy of the estimation model, k-medoids algorithm with weighting distance for clustering distribution feeders is proposed. Meanwhile, a variable selection method for clustering distribution feeders is discussed, considering the correlation and validity of variables. This paper next modifies the XGBoost algorithm by adding a penalty function in consideration of the effect of the theoretical value to the loss function for the estimation of statistical line loss of distribution feeders. The validity of the proposed methodology is verified by 762 distribution feeders in the Shanghai distribution system. The results show that the XGBoost method has higher accuracy than decision tree, neural network, and random forests by comparison of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Absolute Percentage Error (APE) indexes. In particular, the theoretical value can significantly improve the reasonability of estimated results.

Technologies used in Smart Grid to implement Power Distribution System

<span style="line-height: 107%; font-family: 'Arial',sans-serif; font-size: 9pt; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Recently, the debate has been going on about the role of power plus distribution systems, its technologies for future smart grids in power systems. The emerging of new technologies in smart grid and power distribution systems provide a significant change in terms of reduction the commercial and technical losses, improve the rationalization of electricity tariff. The new technologies in smart grid systems have different capabilities to increase the technological efficiency in power distribution systems. These new technologies are the foreseeable solution to address the power system issues. This paper gives a brief detail of new technologies in smart grid systems for its power distribution systems, benefits and recent challenges. The paper provides a brief detail for new researchers and engineers about new technologies in smart grid systems and how to change traditional distribution systems into new smart systems.</span>

Performance assessment of smart distribution networks with strategic operation of battery electric vehicles

Energy security, greenhouse gas emission, and debate on climate change gained an increased interest for penetrating renewable power generation into distribution networks. Among the renewable power technologies, the wind power takes a considerable share to supply the electricity demand in many networks. The wind is intermittent and its power output needs an auxiliary support to serve the energy customers and to limit impacts. The emerging battery electric vehicles (BEVs) are a potential source that can be used to mitigate intermittent effects of wind power outputs in modern distribution networks. BEVs are inherited with the mobility advantage and they can be used as a battery-based energy storage solution to smooth intermittent power outputs. Taking into account the mobility advantage of BEVs, the paper investigates the strategic ability of them to enhance the reliability performance of an unbalanced power distribution network by incorporating the provision of smart coordination of vehicles. Operation of BEVs is characterized by using charging and discharging rates, depth of discharge, and their states of charge at times of operation. A set of case studies were performed by integrating generic distribution network models to a power transmission network model. The results suggest that strategic operation of BEVs can potentially provide a considerable opportunity to build capacity required for wind power in smart power distribution systems. Results also depict that the necessary BEV capacity to mitigate wind impacts on reliability does not necessarily correlate with installed capacities of wind plants. A quantitative assessment provides detailed information of impact thresholds.

Research on Full Data Planning Stimulation State of Smart Distribution Automation and Stimulation Evaluating System

Smart distribution automation is an important part of smart grids. In our country, power distribution network is open looped and radial. To optimize the power distribution network, this paper aims to conduct full data planning in smart distribution automation from the perspective of annual total cost to explore efficient and practical planning algorithm, and provide fundamental basis for development and completion of smart power distribution system. In this paper, firstly, we analyze the proposal background and features of smart distribution automation to prepare for limits and targets in the process of distribution network planning. Then, according to regional characteristics of smart distribution automation, we provide with appliance strategies of multi ant colony algorithm in planning of smart distribution automation and figure out the stimulation branch circuit data of full data planning in certain power supply region after 500 iterations. It turns out that the convergence of algorithm and the result of optimization are good. At last, after evaluating the result of distribution network planning, we make out an index evaluating system and come up with evaluating process of the planning results as well as algorithm principle of AHP, and finally provide fundamental basis for self-cure of smart distribution automation.