Smart Grid Environment Research Articles

Outlier Data Treatment Methods Toward Smart Grid Applications

In a smart grid environment, advanced metering infrastructure (AMI) and intelligent sensors have been deployed extensively. As a result, large-scale and fine-grained smart grid data are more convenient to be collected, in which outliers exist pervasively, caused by system failures, environmental effects, and human interventions. Outlier deletion is always implemented in data preprocessing for improving data quality. However, due to the fact that real records that reflect rare and unusual patterns are also recognized as outliers, outlier mining is necessary to be carried out with the aim of discovering knowledge on abnormal patterns in power generation, transmission, distribution, transformation, and consumption. To the best of our knowledge, a comprehensive and systematic review of outlier data treatment methods is still lacked in the smart grid environment. We, in this paper, aim at presenting the review of outlier data treatment methods toward smart grid applications and categorize them into outlier rejection and outlier mining groups. Since we do this survey from the perspective of data-driven analytics and data mining methods, information security technologies are barely discussed in this paper. Based on a general overview of outlier data treatment methods, we make the contribution of providing the application scenarios of outlier rejection and outlier mining in the smart grid environment. With the construction of smart grid throughout the world, dealing with outlier data has become more crucial for the security and reliability of power system operation. Therefore, we also discuss some future challenges of outlier data treatment toward smart energy management.

Optimal Planning and Operation of Distribution Systems Considering Distributed Energy Resources and Automatic Reclosers

This paper presents an integrated methodology for planning and operation of distribution systems in an environment of smart grids. The decision variables considered are location and sizing of technologies such as: distributed generation (wind, solar and small scale hydroelectric), energy storage, protection elements for fault isolation and automatic reclosers for load transfer. The integration of these technologies enable a higher automation of the distribution system the which in turn brings advantages such as reduction in the energy and active power losses and improvement in the voltage profile and service quality. The methodology consists of three stages: i) a specialized genetic algorithm for location of devices, ii) a particles swarm optimization algorithm for dimensioning of elements and iii) a non-dominated sorting genetic algorithm to solve the multi-objective problem associated with the location of reclosers. The tests were performed on a system of 102 nodes to verify the performance of the proposed methodology.

Optimal Power Network Decomposition for Voltage and Reactive Power Control with Predator-Prey Brain Storm Optimization

Abstract This paper proposes an efficient method for optimizing transmission power network decomposition for voltage and reactive power control (VQC). To realize the distributed control, the idea of power network decomposition becomes much more important. It may be expressed as a combinatorial optimization problem of the NP-hard. As a result, more sophisticated methods are required to solve it in the Smart Grid environment. In this paper, an efficient metaheuristic method is proposed for optimal network decomposition for VQC. As the optimizer, the proposed method makes use of Predator-Prey Brain Storm Optimization (PPBSO) that is an extension of Brain Storm Optimization (BSO) stemmed from the process of human’s brain storming. PPBSO applies the Predator-Prey strategy to BSO so that the global swarm information is improved during the search process. The effectiveness of the proposed method is demonstrated in the IEEE sample power systems.

Darrieus-type vertical axis rotary-wings with a new design approach grounded in double-multiple streamtube performance prediction model

Exploitation of wind energy with vertical axis rotary-wing has advantage over horizontal axis rotary-wing in areas where the wind is turbulent and unstable, i.e., with fast changes either in direction or velocity as normal happens in urban areas. The Darrieus-type vertical axis rotary-wing is experiencing a growth in interest for development and installation due to a growing interest in decentralizing energy conversion. This growth is expected to be in further augment in what concerns the way of the future, i.e., the smart grid environment. A problem linked with this Darrieus-type rotary-wing is the complexity in the performance prediction study, since the blades move around the rotor in 360°. An approach to the double-multiple streamtube performance prediction model for the vertical axis rotary-wing is offered in this paper, offering a flexible adapted tool when the airfoils lift and drag data are not available, or when more complex blade profiles of rotary-wings are in development. A new Darrieus-type vertical axis rotary-wing design is carried out with the approach offered, allowing for a self-start capable blade profile, having an adequate performance at high tip speed ratios. Several field tests are offered providing validation to the self-start, low noise and stable performance of the new rotary-wing design. Also, a modeling, control and simulation of grid integration are presented.

Power Dispatch Strategy for Interconnected Microgrids Based Hybrid Renewable Energy System

This paper proposes a studied system made up of interconnected microgrids (MGs) in which each one includes a hybrid renewable energy system (HRES). The proposed power dispatch strategy ensures load satisfaction in each MG by maintaining the equilibrium between production and consumer demand powers via interactions among MGs and the utility grid. The purpose of this paper is to provide an adequate supply to the different MGs by adapting production to consumption according to the available hybrid power production. This is done with the consideration of the dispatch of the power exchanged among MGs and the main grid. The cooperation between the multiples MGs ensures higher resilience and flexibility of the whole distribution system. The case study results are provided to show the feasibility of this proposed power dispatch strategy in the smart grid environment.

Predictive maintenance strategy of running fault based on ELM algorithm for power transformer

Transformer is the most important core equipment of power system, and once the fault happened, the economic losses and adverse social impacts resulted in faults by the twinkling of eye are difficult to estimate. In order to prevent transformer fault happened, the paper explored a sort of predictive maintenance strategy of transformer fault power transformer supported by the extreme learning machine (ELM) algorithm. In this paper, it made the anatomy of the drawbacks in breakdown maintenance (BM) and preventive maintenance (PM) maintenance system, pointed out the advantage of predictive reliability maintenance (PRM) maintenance system, studied on prediction of the fault diagnosis and predictive algorithm based on ELM, discussed the dynamic fault diagnosis model of power transformer in smart grid environment. A comparative study of different fault pattern prediction algorithms confirmed the rationality and feasibility of the fault prediction algorithm based on ELM. Transformer operation experience shows that the ELM algorithm can provide powerful technical support for the maintenance strategy of transformer fault prediction.

A Novel Automatic Generation Control Method Based on the Ecological Population Cooperative Control for the Islanded Smart Grid

To achieve automatic generation control coordination in the islanded smart grid environment resulted from the increasing penetration of renewable energy, a novel ecological population cooperative control (EPCC) strategy is proposed in this paper. The proposed EPCC, based on the new win‐loss criterion and the time tunnel idea, can compute the win‐loss criterion accurately and converge to Nash equilibrium rapidly. Moreover, based on a multiagent system stochastic consensus game (MAS‐SCG) framework, a frequent information exchange between agents (AGC units) is implemented to rapidly calculate optimal power command, which achieves the optimal cooperative control of the islanded smart grid. The PDWoLF‐PHC(λ), WPH strategy (wolf pack hunting), DWoLF‐PHC(λ), Q(λ)‐learning, and Q‐learning are implemented into the islanded smart grid model for the control performance analysis. Two case studies have been done, including the modified IEEE standard two‐area load frequency control power system model and the islanded smart grid model with distributed energy and microgrids. The effectiveness, stronger robustness, and better adaptability in the islanded smart grid of the proposed method are verified. Compared with five other smart ones, EPCC can improve convergence speed than that of others by nearly 33.9%–50.1% and the qualification rate of frequency assessment effectively by 2%–64% and can reduce power generation cost.

Performance analysis of Hamming code for WSN-based smart grid applications

Many methods have been employed to detect, compare, and correct errors to increase communication reliability and efficiency in wireless sensor networks (WSNs). However, to the best of our knowledge, no existing study has compared the performance of error control codes by using different modulation techniques in a smart grid communication environment when multichannel scheduling is used. This paper presents a detailed performance evaluation and makes a comparison of different modulation techniques, such as frequency shift keying (FSK), differential phase shift keying (DPSK), binary phase shift keying (BPSK), and offset quadrature phase-shift keying (OQPSK), using Hamming codes in a 500-kV line-of-sight substation smart grid environment with multichannel scheduling. A link-quality-aware routing algorithm is used as a routing protocol and a log-normal shadowing channel is employed as a channel model. Simulations are performed in MATLAB and the performance of the Hamming code with various modulation techniques is compared with the results obtained without using any error correction codes for throughput, delay, and bit error rate. The results show that the performance of the Hamming code with OQPSK modulation is better than its performance with other modulation techniques. Moreover, the results show that the performance of Hamming code improves with multichannel scheduling for all modulation techniques.

Development and implementation of advanced metering infrastructure for efficient energy utilization in smart grid environment

Effective utilization and management of energy has become an important issue throughout the world. This paper presents a methodology to effectively monitor and manage energy consumption by using advanced metering infrastructure (AMI). The electrical data such as voltage, current, power, and power factor that are derived from the energy utilization made by household appliances are considered to be the parameters for developing a smart meter, which is found to be the fundamental framework of AMI. These metered data are used to develop an AMI framework in a more intelligent and cost-effective manner. WiFi and Global System for Mobile communication network are integrated within the system so as to facilitate better communication between the consumer and electricity board. The developed system can act as a candidate solution to detect the energy theft from demand side, and it is achieved with the help of support vector machine technique. This paper also proposes a prepaid utilization of energy on demand basis, which could greatly reduce the power consumption levels.

Design and Modeling of MEMS-Based Trace-Level Moisture Measurement System for GIS Applications in Smart Grid Environment

This paper presents a system to monitor the presence of trace moisture in SF6 gas insulated switchgear (GIS) employing a micro-cantilever sensor. The main objective of the proposed system design is to continuously monitor the moisture concentration inside the GIS for enhanced safety. Microcantilever-based sensors reduce the overall size of the measurement system as well as enhance the measurement accuracy and sensitivity. A new system approach is proposed to detect trace moisture by monitoring the change in Q-factor of the microcantilever. It makes the measurement of resonant frequency of the microcantilever easier to implement and analyze. The system is made Internet of Things (IoT) enabled using Raspberry Pi. It allows easy local and remote accessibility of sensor data. The usefulness of the proposed system lies in its continuous monitoring ability, compact nature, and easy integration and accessibility, which are some of the essential and current requirements of the smart grid environment.

EVs for frequency regulation: cost benefit analysis in a smart grid environment

Vehicle-to-grid systems facilitate efficient and reliable integration of electric vehicle (EV) into the smart grid. This integration helps provide various services such as peak load levelling, frequency regulation (FR) and other ancillary services that provide notable benefits to utilities. In addition to the benefits to the utilities, EV owners may also benefit from providing these ancillary services to the grid. In this study, a comprehensive assessment of the economic benefits of using EVs to support FR service to the power grid is developed. The limitations for providing such services to the grid are evaluated. The number of the charge and discharge cycles of the EV battery is estimated based on a realistic semi-logarithmic model. Finally, the estimates are used to calculate the battery degradation cost for providing FR and estimate the safe amount of power that EVs can supply with adequate consideration for daily driving requirements.

Secure Three-Factor User Authentication Scheme for Renewable-Energy-Based Smart Grid Environment

Smart grid (SG) technology has recently received significant attention due to its usage in maintaining demand response management in power transmission systems. In SG, charging of electric vehicles becomes one of the emerging applications. However, authentication between a vehicle user and a smart meter is required so that both of them can securely communicate for managing demand response during peak hours. To address the above mentioned issues, in this paper, we propose a new efficient three-factor user authentication scheme for a renewable energy-based smart grid environment (TUAS-RESG), which uses the lightweight cryptographic computations such as one-way hash functions, bitwise XOR operations, and elliptic curve cryptography. The detailed security analysis shows the robustness of TUAS-RESG against various well-known attacks. Moreover, TUAS-RESG provides superior security with additional features, such as dynamic smart meter addition, flexibility for password and biometric update, user and smart meter anonymity, and untraceability as compared to other related existing schemes. The practical demonstration of TUAS-RESG is also proved using the widely accepted NS2 simulation.

A novel two-stage structure for coordination of energy efficiency and demand response in the smart grid environment

In recent years, Energy Efficiency Programs (EEPs) have been greatly expanded in multifarious aspects. Here, EEPs is considered as a virtual resource affecting operating expenditures of the power system. On the other hand, under the smart environment, Demand Response (DR) programs are also contemplated as virtual power plants in energy policy decisions ranging from short term to long term. Therefore, in this paper, a novel integrated model of demand in presence of EEPs as well as DR programs has been nominated. The presented model is handled in a Two-Stage Structure to Coordinate the EEPs and DR programs, namely TSSCDREEP. In the first stage, the level of Energy Efficiency Investment (EEI) is determined over the midterm horizon time. In the next stage, regarding the rate of EEI, a unit commitment problem incorporating EEPs as well as DR programs is handled while the interactions between EEPs and DR have been scrutinized. The proposed problem is implemented in GAMS. To evaluate the capability of EEPs and DR programs coordination, several analysis are carried out on the IEEE 10-unit to confirm the capability of the proposed framework from economic aspect.

MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things

The recent advances in internet of things (IoT) and industrial wireless sensor networks (IWSNs) paradigm provide a promising opportunity for upgrading today’s elderly electricity industrial systems and even allow the fourth stage of the industrial revolution, referred to as smart grid industry (SGI) 4.0. In SGI 4.0 paradigm, the WSNs are considered as promising solutions due to their advantages, such as cable replacement, ease of deployment, flexibility, and cost reduction. However, harsh and complex smart grid (SG) environments pose great challenges to guarantee reliable communication for WSNs-based SG applications due to equipment noise, electromagnetic interference, multipath effects and fading in SG environments. This results in deteriorating the quality-of-service (QoS) requirements as well as the network lifetime of multi-hop communication-based WSNs for SG applications. Thus, for SGI 4.0 paradigm to come true, a WSN-based highly reliable communication infrastructure is crucial that will wirelessly connect and integrate power system components for more efficient, reliable, and intelligent operations of the next-generation electricity power grids. To address these challenges, in this paper a novel multi-mobile sinks-based QoS-aware data gathering protocol (called MQRP) for WSNs-based SG applications has been proposed to empower SGI 4.0. The extensive simulations study is carried through a network simulation tool called EstiNet9.0. The obtained experimental facts show that the proposed scheme has not only improved the QoS performance metrics, such as packet delivery ratio, memory utilization, control message overhead, residual energy, network lifetime, and throughput, but also reduced packet error rate and end-to-end delay compared to existing data collection schemes.

FGDA: Fine-grained data analysis in privacy-preserving smart grid communications

In a smart grid environment, smart meters periodically collect and report information such as electricity consumption of users to a control center for timely monitoring, billing and other analytical purposes. There is, however, a need to ensure the privacy of user data, particularly when the data is combined with data from other sources. In this paper, we propose a new fine-grained data analysis (hereafter referred to as FGDA) scheme for privacy preserving smart grid communications. FGDA is designed to compute multifunctional data analysis (such as average, variance, and skewness) based on users’ ciphertexts, as well as supporting fault tolerance feature. We remark that FGDA can still function when some smart meters fail. Compared to existing schemes providing both the properties of multifunction and fault tolerance, FGDA is more efficient in terms of computation overheads. This is because FDGA does not use bilinear map or Pollard’s lambda method during decryption. We also demonstrate that FGDA achieves a higher communication efficiency, as the gateway only needs to send the ciphertext to the control center once even for different statistical functions.

A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions

This paper presents a new procedure for finding the optimal placement of the phasor measurement units(PMUs) in modern power grids to achieve full network observability under normal operating conditions,and also abnormal operating conditions such as a single line or PMU outage, while considering the availabilityof PMU measuring channels. The proposed modeling framework is implemented using the fuzzybinary linear programming (FBLP) technique. Linear fuzzy models are proposed for the objective functionand constraints alike. The proposed procedure is applied to five benchmark systems such as theIEEE 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus. The numerical results demonstrate that the proposedframework is capable of finding a fine-tuned optimal solution with a simple model and acceptable solutioncharacteristics compared with early works in the literature. Besides, four evaluation indices areintroduced to assure the various criteria under study such as the observability depth, measurement redundancy,and robustness of the method under contingencies. The results show that full network observabilitycan be met under normal conditions using 20% PMUs penetration; however, under contingencies,approximately 50% PMUs penetration is required. The novelty of the proposed procedure has proven thecapability of the proposed linear fuzzy models to find better optimal number of PMUs with lower numberof channels compared to other algorithms under various operating conditions. Hence, the proposedwork represents a potential tool to monitor power systems, and it will help the operators in a smart gridenvironment.

Distributed Generation Units as Ancillary Services Providers in a Pre Smart Grid Environment

AbstractNowadays, ancillary services in electrical distribution networks (e. g. voltage support and reactive power control), usually provided by capacitor banks, start to be performed by distributed generation units (DGs). In this way, several papers have been studying the use of DGs as reactive power providers, and the power electronic/market regulation involved in this new scenario. However, the authors commonly consider a full implementation of Smart Grid philosophy, i. e., there are appropriate communications between DGs and distribution network operator (DNO)’s control centers, but it is not a close reality in many developing countries, due to high costs involved in their implementation. Therefore, this paper proposes a new method in order to use DGs as ancillary services providers in a short and medium-term (called in the literature Pre Smart Grid), in which there are not effective communications between DGs and control centers of DNOs. The proposed method uses a non-uniform DGs distribution, obtained from local atlas of wind, solar, hydraulic and biomass power. The methodology presented accurate results when compared with a PSO-based method, widely used to solve optimization problems, but needs a complete Smart Grid philosophy implementation to work.

Dynamic demand response in smart buildings using an intelligent residential load management system

In a smart grid environment, the utilities, which electric power distribution companies with different pricing strategies, are interested in demand side management (DSM) schemes to achieve reduction in peak demand. Utilities provide additional benefits and encourage the consumers to have direct interaction with the grid by actively participating in DSM programs. An intelligent residential load management system (IRLMS) is proposed for consumers to attain reduction in electricity bill and avail more incentive from the utility by responding to their DSM schemes. The IRLMS attempts to reduce the electricity bill and maintain the total load under maximum demand limit by scheduling the schedulable loads while considering the operating dynamics of non-schedulable loads. The IRLMS scheduler is built with two different techniques, namely priority-based scheduling algorithm and optimisation-based scheduling algorithm for different utility tariff methods such as flat rate tariff and real time pricing, respectively. The IRLMS model is implemented in MATLAB and the results illustrate significant benefits for both consumers and utility.

Distributed Topology Control Techniques for Wireless Sensor Networks: An Overview

The recent technological innovations and development like the Internet of Things (IoT), Smart Grids and Smart Environment applications tend to enhance the scalability, self-organizing capability of the mesh networks as well as the wireless sensor networks. As the wireless sensor network have limited energy resources, there is a need of utilizing these limitations in an efficient way in order to accomplish such advanced energy efficiency and better lifetime can be taken care by the operation of topology control methods. In this survey the issue of energy efficiency is formed upon and a comprehensive study of topology control technologies that can be used for enhancement of battery lifetime in WSNs is presented. An insight of how energy efficiency can be achieved by design is provided by review of the significant topology control algorithms based on the energy conservation approach adopted, those algorithms are classified further and evaluated on the basis of communication so as to aid the designer to select best suitable techniques for their applications. A number of open research issues for achieving the energy efficiency by means of controlling topology are identified based on the survey conducted.

Proposed DSO ancillary service processes considering smart grid requirements

The role of distribution system operator (DSO) in a smart grid environment includes five services, namely, flexibility services, infrastructure provision for electric vehicles, energy efficiency services, ownership and management of metering equipment, and data handling. An important role of DSOs in a smart grid environment is the flexibility services, which are also required by transmission system operator (TSO), suppliers and balancing responsible parties. With different objectives this may, at times, lead to conflicting interests. To safeguard the proper functioning of the energy system, a form of coordination will be required to address and resolve these conflicting interests. A proposed DSO ancillary service processes are identified considering smart grid requirements. The intelligence of the smart grid is critically reliant on geospatial data to represent and track numerous devices' locations within the connected model of the distribution system. Geographical information system is an essential tool for handling the proposed DSO ancillary service processes in an efficient way. An integrated process flow diagram for the proposed DSO ancillary services together with TSO ancillary services is developed.