Grid Environment Research Articles

Grid Load Reduction through Optimized PV Power Utilization in Intermittent Grids Using a Low-Cost Hardware Platform

Renewable energy incorporation in many countries takes different forms. In many developed countries, grid-tied solar photovoltaic (PV) installations are widely coupled with lucrative Feed-in-Tariffs (FiT). However, conventional grid-tied solutions are not readily viable in many developing countries mainly due to intermittent grids with load shedding and, in some cases, lack of net-metering or FiT. Load shedding refers to an intentional electrical power shutdown by the utility company where electricity delivery is stopped for non-overlapping periods of time over different parts of the distribution region. This results in a non-continuous availability of the utility grid for many consumers over the course of a day. In this work, the key challenges in the integration of solar energy explicitly in residential power back-up units are reviewed and system hardware level requirements to allow optimized solar PV utilization in such intermittent grid environments are analyzed. Further, based upon the low-cost sensing and real-time monitoring scheme, an online optimization framework enabling efficient solar incorporation in existing systems to achieve minimum grid dependence in intermittent grid environments is also provided. This work is particularly targeted for over 1.5 billion residents of semi-electrified regions in South Asia and Africa with the weak and intermittent grid.

Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method.

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.

Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data

With the emergence of the smart grid environment, smart meters are considered one of the main key enablers for developing energy management solutions in residential home premises. Power consumption in the residential sector is affected by the behavior of home residents through using their home appliances. Respecting such behavior and preferences is essential for developing demand response programs. The main contribution of this paper is to discover the association between appliances’ usage through mining temporal association rules in addition to applying the temporal clustering technique for grouping appliances with similar usage at a particular time. The proposed method is applied on a time-series dataset, which is the United Kingdom Domestic Appliance-Level Electricity (UK-DALE), and the results that are achieved discovered appliance–appliance associations that have similar usage patterns with respect to the 24 h of the day.

Computational grid scheduling architecture using MapReduce model-based non-dominated sorting genetic algorithm

Computational grid (CG) environment has a group of autonomous dissimilar distributed computing systems to provide service to user tasks. To attain the auspicious usefulness of CG resources, basically best scheduling algorithms are important. The CG service users are very cautious in time needed for task completion. To meet multiple requirements of the user, the job allocation problem in CG is designed as a multi-objective problem. Evolutionary algorithms have an efficient meta-heuristic technique for optimization problem solving. This work introduces a MapReduce model for non-dominated sorting genetic algorithm (NSGA-II) for independent job scheduling in a CG. This research work attempts to find the optimal schedules by considering makespan and flowtime minimization. A fuzzy membership function is used to analyze the efficiency of the schedule. Set of benchmark instances is used to test the algorithm. Experimental results show that MapReduce model-based NSGA-II generated finer solutions in less time than the MapReduce model-based weighted sum multi-objective genetic algorithm (WMOGA) which is also implemented in this paper.

Software Architectures for Smart Grid System—A Bibliographical Survey

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems, in which system architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope with current Information and Communication Technologies (ICT). The distributed generation in a smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible, and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review of architectures for smart grids. An in depth analysis of layered and agent-based architectures based on the National Institute of Standards and Technology (NIST) conceptual model is presented. Also presented is a set of smart grid Reference Architectures dealing with cross domain technology.

Enhancing the security of a cloud‐based smart grid AMI network by leveraging on the features of quantum key distribution

AbstractIn the last decade, there have been many research proposals on the potentials of incorporating cloud computing technology to the electrical power grid as it evolves rapidly to the smart grid. The advanced metering infrastructure (AMI) is one domain of the smart grid that can be greatly impacted by cloud computing technology with regards to an improved capability for data storage and computations, which will be a serious burden to the AMI in an evolved smart grid environment. In our previous study, a cloud‐based OpenFlow firewall for the mitigation of distributed denial‐of‐service attacks in a smart grid AMI (SG AMI) was proposed. In that study, it was clearly demonstrated that cloud computing technology, in addition to improving the storage and computation capability of the AMI, can also be leveraged on to enhance the security of the SG AMI in an attack scenario. In this study, we propose a cloud‐based SG AMI system model utilizing quantum key distribution (QKD) cryptography for enhancing the security of a cloud‐based SG AMI. The proposed model has been designed to complement the security features of the previously designed OpenFlow firewall to guarantee not only the availability but also the confidentiality and integrity of data traversing the AMI network and beyond. The analysis on computation and communication costs shows that the protocols utilized in the proposed system model are lightweight protocols. In addition, results from the simulation of the QKD (BB84) protocol show that QKD can be leveraged on to provide information theoretic security for the cloud‐based SG AMI.

Neural controller for the smoothness of continuous signals: an electrical grid example

In this paper, the use of artificial neural networks (ANNs) is proposed to manage the local demand of different electric grid elements to smooth their aggregated consumption. The ANNs are based on the load automation of the local electric behavior, following a local strategy but affecting to the global system. In an electrical grid, there is no possibility to share information between the users because anonymity must be warranted. Therefore, a solution to the problem is elaborated with the minimum information possible without the need for communication between the users. A grid environment and behavior of different users is simulated.

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

An Automated Parameter Tuning Method for Ant Colony Optimization for Scheduling Jobs in Grid Environment

Multiloop current control for an inductive–capacitive–inductive‐filtered grid‐connected inverter with frequency‐adaptive capability under distorted grid environment

To eliminate the adverse effects of grid voltages such as the harmonic distortion and frequency variation, this study presents a multiloop current control scheme for an inductive-capacitive-inductive-filtered grid-connected inverter. The proposed scheme is achieved by multiloop control structure which is composed of the grid-side current control loop, capacitor voltage control loop, and inverter-side current control loop. This structure has an advantage of seamless mode transfer between the grid-connected mode and islanding mode. In addition, the proposed scheme offers a frequency adaptation capability even under distorted grid environment, providing a high quality of injected current in grid-connected mode. Whereas the conventional schemes require lots of state measurements, the proposed scheme eliminates the need of additional sensing devices by employing a discrete-time state observer. The resonant controllers are implemented in outer grid-side current controller to deal with harmonically distorted grid voltage at the 5th, 7th, 11th, and 13th orders. The proposed scheme ensures the quality of grid currents even in the presence of both the harmonic distortion and frequency change in grid voltages by using only the measurement of the grid-side currents and grid voltages. Simulation and experimental results are given to validate the usefulness of the proposed scheme.

Smart Load Node for Nonsmart Load Under Smart Grid Paradigm: A New Home Energy Management System

This article presents a novel approach for efficient operation of non-smart household appliances under smart grid environment using the proposed smart load node (SLN). In real world scenario, there are so many non-smart loads currently in use and embedding appliance specific intelligence into them to make them as smart loads will be more expensive compared to the proposed SLN, which is a common solution for all types of non-smart loads. This makes the proposed low-cost SLN, which neither requires any infrastructural change in the electrical wiring of a house nor any constructional change in home appliances at the manufacturing stage and at the consumer end, as a feasible solution for intelligent operation of non-smart home appliances under smart grid environment. The SLNs, which are placed in a home like distributed wireless sensor nodes, form a home area network (HAN). The HAN includes a load management unit (LMU) which acts as master for all distributed SLNs. Wi-Fi is chosen as a medium of communication in the HAN. The LMU incorporates load management algorithm which is written in Python script.

Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting

Short-term load forecasting (STLF) plays an important role in the planning and operation of power systems. However, with the wide use of distributed generations (DGs) and smart devices in smart grid environment, it brings new requirements on the accuracy, quickness and intelligence of STLF. To address this problem, a novel short-term load forecasting method based on attention mechanism (AM), rolling update (RU) and bi-directional long short-term memory (Bi-LSTM) neural network is proposed. Firstly, RU is utilized to update the data in real time, making the input data of the model more effective. Secondly, influence weights are assigned through AM to highlight the effective characteristics of the input variables. Thirdly, a Bi-LSTM is used for model training, and the predicted load values are obtained through the linear transformation layer and softmax layer. Finally, the actual data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia are employed to verify the validity of the method. The results show that the introduction of AM and RU into forecasting model can improve the prediction accuracy. Compared with traditional Bi-LSTM model, both the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of Bi-LSTM model with AM and RU have declined in the load forecasting for the two data sets. And it proves that the proposed method has higher accuracy, less computation time and better generalization ability.

A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.

Robust Investment for Demand Response in a Distribution Network considering Wind Power and Load Demand Uncertainties

Abstract Due to the increasing penetration of wind power into the distribution network, preserving system security and reliability becomes a significant challenge for the system operator. In the smart grid environment, the demand side is required to take more responsibilities to accommodate the uncertainty of wind power generations, known as demand response (DR). To enable this feature in the utility grid, system-wide costs, which include metering, communication and load control system upgrade cost and incentive cost for customers, should be considered in assessing cost-effectiveness. This paper proposes a novel optimization model for demand response facility (DRF) investment to determine the DR sizing and siting. Robust optimization is adopted to maintain overall economic benefit and distribution network operation security. The problem is formulated as a bi-level mixed-integer program. A column-and-constraint generation algorithm (C&CG) combined with outer-approximation (OA) linearization method is employed to solve this problem. Numerical tests on a modified IEEE 33-bus distribution network illustrate the effectiveness and validation of the proposed model.

Multi-objective home energy management with battery energy storage systems

Home energy management (HEM) is one of the crucial requirements for future smart grid environment. Usually, utility companies motivate residential consumers to participate in household demand side management (DSM) techniques. The residential consumers’ main intention is to reduce their electricity bills whereas utilities’ primary goal is to reduce their system peak load demand. The benefits of DSM can be enhanced with smart energy storage systems. In this paper, an intelligent multi-objective household DSM with battery storage systems is proposed to minimize both the objectives simultaneously. Generally, the residential DSM deals with various controllable appliances that normally have different operating time priorities according to the consumers’ preferences. Because of this, the DSM problem is modelled as a mixed integer optimization problem. In this paper, the multi-objective mixed integer linear programming technique (MOMILP) is used to solve the proposed residential DSM problem. The proposed model is tested on a system having four different residential consumers. The benefits of the battery energy storage systems are analysed by taking six different scenarios. An economic analysis of each residential consumer's battery storage system with load uncertainty modelling is also done in this paper. Simulation results of various scenarios with different objectives demonstrate the effectiveness of the proposed model.

HCR-MAC: A High-capacity Receiver-based MAC Protocol for Cognitive Radio Enabled AMI Networks in Smart Grid

The Cognitive Radio (CR) Advanced Metering Infrastructure (AMI) network is considered as a viable solution to enhance various aspects of electric power grid and realize smart grid. However, several challenges are generated due to the harsh wireless environment in smart grid. As a result, throughpu

An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data

In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids.

Large-scale data processing software and performance instabilities within HEP grid environments

Large tasks running on grids and clouds have introduced a need for stability guarantees from geographically spanning resources, where failures are handled pre-emptively. Detecting performance inefficiencies in such cases is difficult. While individual services implement fault-tolerance, the behaviour of interacting failures within tightly-coupled systems is less understood. This paper describes an approach to modelling performance of production tasks running within the ALICE grid. We provide an overview of the ALICE data and software workflow for production jobs. Event states are then constructed, based on data centre job, computing, storage and user behaviour. We then address the question of analysing failures within the context of operational instabilities, occurring in production grid environments. The results demonstrate that operational issues can be detected and described according to the principle service layers involved. This can guide users, central and data centre experts to take action in advance of service failure effects.

An Improved $\alpha\beta$ -EPLL Based on Active Disturbance Rejection Control for Complicated Power Grid Conditions

The enhanced phase-locked loop (EPLL) adds an amplitude estimate to the conventional phase-locked loop (PLL), which solves the problem of double-frequency disturbance in the Phase-Locked Loop mathematical model at steady state. However, there are few researches on disturbance resistance design of EPLL in complicated grid environment. Therefore, periodic disturbances of different frequencies are introduced into frequency estimation and amplitude estimation of EPLL when the grid voltage has DC offset, three-phase unbalance, high-order harmonic disturbance and sub-synchronous oscillation (SSO). This will interfere with the frequency and amplitude estimation performance of the EPLL, and then affect the closed-loop control and frequency tracking of the converter. In order to improve disturbance rejection of the EPLL. Firstly, the mathematical model of the disturbance in the frequency and amplitude loop is deduced and analyzed. Then the active disturbance rejection control (ADRC) is used to improve the EPLL. Through the simulation model of MATLAB/Simulink, the parameters of the ADRC and the ESO are compared and validated. The proposed EPLL based on ADRC is verified experimentally for the tracking performance under the frequency stability or drop in complicated power grid environment.

Fault-tolerant multi-agent scheme for leader/follower formation control of homogeneous mobile robot team

This paper deals with fault-tolerance and formation control problems for homogeneous mobile robots under leader/follower failures by using the multi-agent paradigm. These issues are addressed by a cooperative approach supported by two kinds of agents: 1) robot agents (classified into a leader and a set of followers depending on their objective function), assigned to robots for collecting information, making decisions and transferring them to physical robots for execution; 2) supervisory agent (an interface between the control system and the team), synchronises and coordinates tasks of the other agents. These agents interact by a negotiation protocol to satisfy their objectives: 1) preserve predefined leader/follower formation; 2) conserve robots order inside the formation; 3) manage dynamic disturbance events (e.g., unexpected breakdown, integration of new robots). To validate this scheme and evaluate its performances, different simulations are examined by using ten robots evolving inside a grid environment cluttered with static obstacles with/without failures.

COMPASS Production System Overview

Migration of COMPASS data processing to Grid environment has started in 2015 from a small prototype, deployed on a single virtual machine. Since summer of 2017, the system works in production mode, distributing jobs to two traditional Grid sites: CERN and JINR. Now the infrastructure of COMPASS Grid Production System includes 6 virtual machines, each is reserved for one production service: database, PanDA, Auto Pilot Factory, Monitoring, CRIC information system and, finally, production system (ProdSys) management instance, which provides a user interface for production manager and hosts services of automatic processing. Support of COMPASS virtual organization is provided by CERN IT. CRIC is also deployed at CERN Cloud Service. Other ProdSys services are deployed at JINR Cloud Service. There are two storage elements at CERN: EOS for short-term storage and Castor for long-term storage. During last year, along with providing a 24/7 service, the system was instrumented by many features, which allow automating data processing as much as possible. Recently, Blue Waters HPC has become a member of the computing infrastructure of the experiment. Details of implementation, workflow management, and infrastructure overview are presented in this article.