Smart Grid Load Research Articles

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

Load balancing of renewable energy: a cyber security analysis

BackgroundIn the coming years, the increase of automation in electricity distribution grids, controlled by ICT, will bring major consequences to the cyber security posture of the grids. Automation plays an especially important role in load balancing of renewable energy where distributed generation is balanced to load in a way that the grid stability is ensured. Threats to the load balancing and the smart grid in general arise from the activities of misbehaving or rouge actors in combination with poor design, implementation, or configuration of the system that makes it vulnerable. It is urgent to conduct an in-depth analysis about the feasibility and imminency of these potential threats ahead of a cyber catastrophy. This paper presents a cyber security evaluation of the ICT part of the smart grid with a focus on load balancing of renewable energy.MethodThe work builds on a load balancing centered smart grid reference architecture model that is designed as part of the evaluation with the help of SCADA system and smart grid experts. The smart grid load balancing architecture represented by the model is then analyzed using a threat modelling approach that is encapsulated in a tool called securiCAD. Countermeasures are introduced in the model to measure how much each improve the cyber security of the smart grid.ResultsThe analysis shows that the main threat comes from the internet and is directly dependant on the level of internet access office users have coupled with their level of access on the OT zone. Supply chain attacks are also of great concern, i.e. the compromising of the software/hardware vendor with the objective of feeding rogue updates to assets, typically to install a backdoor. The general takeaway defense-wise is that it is of the utmost importance to increase efforts in securing the smart Grid in all the ways possible as they appear to be generally complementary.DiscussionThe obtained results raise concerns whether the architecture of the smart grid still remains satisfactory in today’s state of the cyberspace and the increased presence and sophistication of cyber threats. There are also concerns whether the proposed security measures, regardless of their evaluated effectiveness, are realistically implementable from both financial and practical point of view.ConclusionsThere is no silver bullet available to achieve full protection against cyber attacks. The smart grid remains a network of IT/OT machines with dataflows going between them. State-sponsored hackers given enough time and regardless of the defences in place will eventually make their way into a critical infrastructure such as the smart grid. While mitigations will not eliminate the threats, they will increase the cyber resilience of the infrastructure by increasing both its time frame and effectiveness. As such, in a time where usability, efficiency and practicality are at the front of every domain, innovations regarding these aspects should really be carried out with strong security in mind.

Smart grid load balancing methods to make an efficient heterogeneous network by using the communication cost function

Different node types in smart grid (SG) system have different communication necessities. Different type of information such as power grid status and demand requests and responses are established between control station (CS) and different SG node types. Assigning the certain percentage of the nodes traffic to the radio access technologies (RATs) with respect to their cost functions (CFs) value can help to achieve this goal by defining the highest fitness degree between SG node type communication requirements and RATs communication characteristics to have an efficient load balance. In this study, the key performance indicators (KPIs) for CFs are the SG node communication requirements which are defined as data rate, delay, reliability, and security. The value of the work can be seen in node assigning method based on the CFs as a function of different RAT communication characteristics and node type communication requirements. The highest fitness degree between node communication needs and RAT characteristics are achieved in an efficient way which causes to make an efficient heterogeneous network. The work impact of using a certain RAT which has the best efficiency in sense of communication aspects by using a well-defined CF is the innovation of this study as well.

Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM

The usage of information and communication technology (ICT) in the power sector has led to the emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues such as–increasing demand–supply gap, grid instability, and deteriorating quality of service persist in this network which degrade its performance. These issues can be handled in an efficient way by managing the demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to gather the data generated in IoE network and perform analytics to manage DR. Working in this direction, a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages which work as follows. In the first stage, the residential and PHEV users are identified whose demands can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM benchmark data and Open Energy Information dataset. The simulation results prove that the proposed scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV users.

A Practical Pricing Approach to Smart Grid Demand Response Based on Load Classification

Appropriate pricing schemes are of great importance in terms of managing large-scale and distributed controllable loads in smart grids. In this paper, a practical pricing scheme is proposed to encourage different consumers to participate in demand response by providing them with a list of price plans. First, a classification algorithm is employed to divide consumers into different categories based on their individual information, such as marginal cost, upper limit of load adjustment, and elasticity coefficient. Afterwards, from the perspective of load service entity, a pricing model is formulated as a nonlinear programming problem, aiming to minimize the overall operation cost. Moreover, the Bayesian discrete probability distribution function is adopted to tackle the uncertainty of consumers’ choosing behavior. Meanwhile, an incentive compatible constraint is added to capture the real private information of controllable loads, e.g., inducing them to bid as close as their marginal costs. According to the case studies on the IEEE 30-bus and 118-bus systems, the proposed pricing scheme is an effective demand response approach to managing controllable loads and achieving lower system cost.

A Learning Automaton-Based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids

In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. In our specific instantiation, we consider the scenario when the power system has a local-area Smart Grid subnet comprising of a single power source and multiple customers. The objective of the exercise is to tacitly control the total power consumption of the customers’ shiftable loads, so to approach the rigid power budget determined by the power source, but to simultaneously not exceed this threshold. As opposed to the “traditional” paradigm that utilizes a central controller to achieve the load scheduling, we seek to achieve this by pursuing a distributed approach that allows the users 1 to make individual decisions by invoking negotiations with other customers. The decisions are essentially of the sort, where the individual users can choose whether they want to be supplied or not. From a modeling perspective, the distributed scheduling problem is formulated as a game, and in particular, a so-called “Potential” game. This game has at least one pure strategy Nash equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which utilizes the theory of learning automata (LA), permits the total supplied loads to approach the power budget of the subnet once the algorithm has converged to the NE point. The scheduling is achieved by attaching a LA to each customer. The paper discusses the applicability of three different LA schemes, and in particular, the recently-introduced Bayesian learning automata. Numerical results, obtained from testing the schemes on numerous simulated data sets, demonstrate the speed and the accuracy of proposed algorithms in terms of their convergence to the game’s NE point. 1 In this paper, we use the terms “customers” and “users” interchangeably.

Describing the dynamics, distributions, and multiscale relationships in the time evolution of residential building energy consumption

Residential buildings may be described as complex social-technological systems expressing component interdependence and chaotic temporal variability. As such, we characterized the dynamics and multiscale relationships of hourly electricity consumption data for 13 occupied Florida houses from calendar year 2013. Statistical approaches included: (1) exploratory data analyses with distribution-based descriptive statistics; (2) normality testing; (3) spectral and monofractal analyses; (4) multifractal detrended fluctuation analyses (MFDFA) with surrogate testing; and (5) Ward’s minimum variance method for hierarchical agglomerative clustering. Results suggested the energy-use patterns were non-normal, nonlinear, and exhibited predominantly anti-persistent fractal complexities. Thus, classical descriptive statistics presuming Gaussian probability density function (PDF) distributions neither well fit, nor well described, the data and their interdependent characteristics. Notably, clusters of comparable houses were categorically and statistically different when using descriptors based on normality (e.g., mean, variance, skewness, kurtosis) versus those based on fractality (e.g., Hurst exponent, multifractal spectrum width). We believe MFDFA statistical outputs may serve as novel indicators of residential building dynamics as they better characterize the complex, nonlinear asset and occupancy interactions and they require no assumptions regarding the PDF distribution shape. We offer guidance on the data management, transformation, parameterization, and interpretation processes necessary to apply MFDFA to whole-house, short-interval, electricity consumption time series data. Multifractal quantification of building performance time series data may be useful on multiple fronts: (1) detecting under-performing households; (2) improving segmentation, targeting, and pre/post analyses of energy efficiency interventions; (3) diagnosing building system failure risks; and (4) improving smart grid supply and load balancing.

Smart Grid Load Forecasting Cloud Platform Architecture: A Review

Load forecasting is making calculations or predictions of a future event or condition based on analysis or study of historical data, events or observations. With the development of smart grid, smart meter is also increasing largely, which can generate metering data per 15 mins, and there comes a huge amount of metering data. Thus such huge amount of data leads to some challenges in data processing. This paper introduces the meaning of studying load forecasting, and recommends current relevant research. Then, the cloud platform architecture, the key technologies based cloud and current relevant research for load forecasting based on cloud in smart grid is discussed. Finally it introduces the related load forecasting services based on the cloud platform architecture in smart grid. After all, there is a conclusion of this paper.

Modeling and simulation of wind turbines and responsive loads in smart grids

In order to implement demand response programs appropriate equipment must be provided to subscribers at any moment be aware of the price of electricity and the price of electricity at different times to an appropriate response to reduce their costs to the network, that This would reduce consumers will demand at peak times. Smart grid using two-way communication and transmission of information to consumers as well as the use of advanced measurement system, reasonable structure has provided for the full implementation of demand response programs. In this paper, a wind turbine that is connected to a smart grid responsive loads to consider and examine the systems in the grid.

Modeling and simulation of photovoltaic systems and responsive loads in smart grid

In this paper, the methods of forecasting output at different times and applications within the smart grid photovoltaic system (PV) systems will be investigated method of calculations used in this short amount of solar radiation to consider the reduction in average for a few hours to compensate for power fluctuations and power reserve or battery operation and supply and demand balance is forecast. In this paper, the Monte Carlo simulation is used to predict the long-term average. Usually the use of the smart grid PV system problems and even load variation problems within the system are accountable in this study, and surpassed Weather amount of output power into a smart grid system used pv is the case in the field of innovation and new research in this paper uses three software HOMER, GAMS and MATLAB simulation of photovoltaic systems within a network smart look and accountability in the network will be supplied by the PV system will be examined.

Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN) based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.

Intelligent Residential Energy Management in Smart Grid

Objectives: Providing efficient energy management for smart home through appropriate scheduling of household appliances is addressed in this paper, with two objectives namely, electricity peak demand minimization and electricity cost minimization. Methods/Statistical Analysis: The residential load scheduling problem requires the prior knowledge about the residential electricity demand and electricity price information, for scheduling the appliances. Two different algorithms namely, Discrete Non-dominated Multi-objective Particle Swarm Optimization (DNMPSO) algorithm and Manhattan distance based Non-dominated Multi-objective Genetic Algorithm (MNMGA) are proposed to solve the problem in this paper. Findings: Both the algorithms were simulated in order to evaluate their performance. The Peak-To-Average Ratio value is used as the measure to assessing the peak load in electricity demand. Based on the results, it is observed that the DNMPSO algorithm obtains significant cost reduction with the acceptable Peak-To-Average Ratio value than the MNMGA algorithm. In addition to that, the DNMPSO algorithm provides better diversity than the MNMGA algorithm. Application/Improvements: The proposed algorithm can be used to enhance the energy management for residential load in smart grid. Further, the cost efficiency can be improved by incorporating renewable energy resource.

Large Scale Control of Deferrable Domestic Loads in Smart Grids

In this paper, we investigate a realistic and low-cost deployment of large scale direct control of inelastic home appliances whose energy demand cannot be shaped, but simply deferred. The idea is to exploit: 1) some simple actuators to be placed on the electric plugs for connecting or disconnecting appliances with heterogeneous control interfaces, including non-smart appliances and 2) the Internet connections of customers for transporting the activation requests from the actuators to a centralized controller. Our solution requires no interaction with home users: in particular, it does not require them to express their energy demand in advance. A queuing theory model is derived to quantify how many users should adopt this solution in order to control a significant aggregated power load without significantly impairing their quality of service.

PSO-Based Smart Grid Application for Sizing and Optimization of Hybrid Renewable Energy Systems.

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers.

Charging demand for electric vehicle based on stochastic analysis of trip chain

With its popularisation, electric vehicle (EV) has been part of the important load of smart grid. The analysis on charging demands of EVs is the basis of the study of influences on power grid and charging infrastructures planning. Charging demand calculation is based on the precise analysis on the driving law of users. This study proposes a method based on the stochastic simulation of trip chain to solve the existing problem. First, the concept and characteristic variables (trip start time, driving time, parking duration, driving distance and trip purpose) of trip chain are introduced. Second, the probability distribution models of these variables are established considering their correlative relationships. Third, using Monte Carlo method, the complete trip chains are simulated, and then the spatial–temporal distributions of charging demands are calculated. Finally, based on the National household trip survey data, and according to the proposed method, charging demands in different areas are studied. The results show that the proposed method can accurately simulate the driving law of users and reflect spatial–temporal distribution characteristics of charging demands, and different charging scenarios will lead to different forms of charging demand distribution, which will exert different impacts on power system.

Distributed MPC of Aggregated Heterogeneous Thermostatically Controlled Loads in Smart Grid

Using aggregated heterogeneous thermostatically controlled loads (TCLs) to provide ancillary services in a smart grid is a promising technique. This paper proposes an improved population model with lockout time considered to describe the aggregate dynamics. Based on that, a distributed model predictive control (DMPC) scheme is investigated to control a population of TCLs for the regulation service. Full derivations of the system model and the DMPC scheme are provided. A flexibility penalty is developed and added to the objective function to preserve the regulation flexibility. Simulation results, tested by real data, reveal the feasibility and effectiveness of the proposed method.

A framework for classification of non-linear loads in smart grids using Artificial Neural Networks and Multi-Agent Systems

This paper proposes a general framework that uses the Artificial Neural Networks (ANNs) as a classification tool of nonlinear loads in a simulated smart grid environment by using Multi-Agent Systems (MAS). The increasing of communication and computation infrastructure on devices installed on modern power distribution systems allows new automated and coordinated control actions. This is mainly due to the ability to manage and process information and deploy actions in real-time mode. One important measurement tool is the smart meter, which will be present with all customers. Besides the measurement function, it has the communication feature and also some computational processing capability. Considering this base structure, the objective is to present methods to classify/identify nonlinear loads based only on current or voltage profiles measured by smart meters in this distributed computing environment. In this work, the MAS will manage the data and the tasks related to the classification and the ANN will perform the classification, both tools have been developed in JADE/JAVA and Matlab environment, respectively. Test case using 4000 input signals distributed in eight classes corresponding to nonlinear medical electromedical loads have been used and 98.7% of the samples have been identified correctly.

DSM in an area consisting of residential, commercial and industrial load in smart grid

With the latest introduction of the demand side management (DSM) in smart grids, the power distribution units are able to modify the load schedules of the consumers. This involves a co-operative interaction of the utility and the consumers so as to achieve customer load modifications in which the customer, utility and society all are benefited. The interaction is performed with the help of the devices known as the smart meter. This paper shows the use of game theory and logical mathematical expressions in order to achieve the objectives. The objectives are to minimize the peak to average ratio (PAR) and the energy cost. The outcome of the game between supplier and customers helps to shape the load profile. The design proposed in this paper is very user-friendly and is based on simple logarithmic programming computations. In this paper, residential, commercial and industrial types of loads are taken into account. A basic 24 h load schedule along with the fluctuating prices at each hour of the day is forecasted by the supplier of the various shiftable and non-shiftable loads and then that schedule is conveyed to the user. The users are encouraged to shift their high load devices to off-peak hours which will not only reduce their electricity costs but also substantially reduce the PAR in the load demand.

Investigation of Microelectromechanical Switches for Next Generation DC Power Distribution System

Abstract This paper investigates the application of microelectromechanical system (MEMS) switches for DC power distribution system. Traditional electromechanical switches, solid state switches and solid state switch array are studied and simulated to understand their characteristics. Performance and characteristics of MEMS switches are reviewed and identified that electrostatically actuated MEMS switches are suitable for DC power applications. Scalable total cross tied (TCT) array configuration using MEMS switches has been proposed. The proposed configuration is suitable for variable voltage/current rating. Arc-less behavior of the switch configuration is analyzed using modified Paschen’s curve. 400 V/6 A system is considered for the simulation and comparative study. The simulated result of the proposed MEMS switch array configuration is compared with the traditional switches. The comparative study shows that the proposed switch array configuration gives better performance in terms of voltage drop, leakage current, power loss, arc and size. This can be used in DC power system protection, circuit breaking, battery protection and smart grid load switching applications.

Optimal RTP Based Power Scheduling for Residential Load in Smart Grid

To match supply and demand, shifting of load from peak period to off-peak period is one of the effective solutions. Presently flat rate tariff is used in major part of the world. This type of tariff doesn’t give incentives to the customers if they use electrical energy during off-peak period. If real time pricing (RTP) tariff is used, consumers can be encouraged to use energy during off-peak period. Due to advancement in information and communication technology, two-way communications is possible between consumers and utility. To implement this technique in smart grid, home energy controller (HEC), smart meters, home area network (HAN) and communication link between consumers and utility are required. HEC interacts automatically by running an algorithm to find optimal energy consumption schedule for each consumer. However, all the consumers are not allowed to shift their load simultaneously during off-peak period to avoid rebound peak condition. Peak to average ratio (PAR) is considered while carrying out minimization problem. Linear programming problem (LPP) method is used for minimization. The simulation results of this work show the effectiveness of the minimization method adopted. The hardware work is in progress and the program based on the method described here will be made to solve real problem.