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Smart Grid, IEEE Transactions on

Issue 3 • Date Sept. 2013

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Displaying Results 1 - 25 of 60
  • Table of Contents

    Page(s): C1 - 1226
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    Freely Available from IEEE
  • IEEE Transactions on Smart Grid publication information

    Page(s): C2
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  • Mitigating Event Confidentiality Violations in Smart Grids: An Information Flow Security-Based Approach

    Page(s): 1227 - 1234
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1825 KB) |  | HTML iconHTML  

    Modern smart grids, by and large, merge physical interconnections and cyber controllers. Invariably, this tight coupling results in cyber commands manifesting in the physical layer as observable changes, leading to possible disclosure of sensitive system settings. Thus, cyber event confidentiality of the smart grid is violated. Attacks on confidentiality can ultimately lead to integrity and availability attacks; with adequate knowledge of the system topology, internal settings, and how the physical layer responds to cyber commands, a malicious adversary gains knowledge to attack the system. This work shows how to develop self-obfuscating systems based on information flow security properties that can mitigate event confidentiality violations in smart grids. View full abstract»

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  • Ramp-Induced Data Attacks on Look-Ahead Dispatch in Real-Time Power Markets

    Page(s): 1235 - 1243
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    This paper presents a new class of false data injection attacks on state estimation, which may lead to financial arbitrage in real-time power markets with an emerging look-ahead dispatch model. In comparison with prior work of cyber attack on static dispatch where no inter-temporal ramping constraint is considered, we propose a novel attack strategy with which the attacker can manipulate, in look-ahead dispatch, the limits of ramp constraints of generators. It is demonstrated that the proposed attack may lead to financial profits via malicious capacity withholding of selected generators, while being undetected by the existing bad data detection algorithm embedded in the state estimator. The feasibility of such cyber attacks and their economic impact on real-time electricity market operations are illustrated in the IEEE 14-bus system. View full abstract»

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  • Smart Grid Data Integrity Attacks

    Page(s): 1244 - 1253
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    Real power injections at loads and generators, and real power flows on selected lines in a transmission network are monitored and transmitted over a SCADA network to the system operator. These are used in state estimation algorithms to make dispatch, re-balance and other energy management system [EMS] decisions. Coordinated cyber attacks on power meter readings can be designed to be undetectable by any bad data detection algorithm. These unobservable attacks present a serious threat to grid operations. Of particular interest are sparse attacks that involve the compromise of a modest number of meter readings. An efficient algorithm to find all unobservable attacks [under standard DC load flow approximations] involving the compromise of exactly two power injection meters and an arbitrary number of power meters on lines is presented. This requires O(n2m) flops for a power system with n buses and m line meters. If all lines are metered, there exist canonical forms that characterize all 3, 4, and 5-sparse unobservable attacks. These can be quickly detected with O(n2) flops using standard graph algorithms. Known-secure phasor measurement units [PMUs] can be used as countermeasures against a given collection of cyber attacks. Finding the minimum number of necessary PMUs is NP-hard. It is shown that p+1 PMUs at carefully chosen buses are sufficient to neutralize a collection of p cyber attacks. View full abstract»

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  • Behavior-Rule Based Intrusion Detection Systems for Safety Critical Smart Grid Applications

    Page(s): 1254 - 1263
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    In this paper, a behavior-rule based intrusion detection system (BRIDS) is proposed for securing head-ends (HEs), distribution access points/data aggregation points (DAPs) and subscriber energy meters (SEMs) of a modern electrical grid in which continuity of operation is of the utmost importance. The impact of attacker behaviors on the effectiveness of a behavior-rule intrusion detection design is investigated. Using HEs, DAPs and SEMs as examples, it is demonstrated that a behavior-rule based intrusion detection technique can effectively trade false positives for a high detection probability to cope with sophisticated and hidden attackers to support ultra safe and secure applications. It is shown that BRIDS outperforms contemporary anomaly-based IDSs via comparative analysis. View full abstract»

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  • Strategic FRTU Deployment Considering Cybersecurity in Secondary Distribution Network

    Page(s): 1264 - 1274
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    This paper is concerned about strategic deployment of feeder remote terminal unit (FRTU) in primary network by considering cybersecurity of distribution secondary network. First, detection of historical anomaly load profile in secondary network is assumed to be observable. These irregularities of historical energy usages can be determined from consumer billing centers using proposed cybersecurity metrics. While it is constrained by budget on the number of FRTUs that can be deployed, the proposed algorithm identifies pivotal locations of a distribution feeder to install the FRTUs in different time horizons. The simulation results show that the infrastructure enhancement using proposed multistage method improves investment planning for distribution systems. View full abstract»

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  • Area-Load Based Pricing in DSM Through ANN and Heuristic Scheduling

    Page(s): 1275 - 1281
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    To meet the fast growing demand of energy, in addition with increased generation, improved efficiency, stability and flexibility, smart techniques need to be adopted that are in compliance with the environment and energy conservation. In this paper, we present an autonomous demand-side energy management to encourage users to willingly modify their electricity consumption without compromising with service quality and customer satisfaction using load forecasting. The projected distributed demand side energy management (DSM) strategy gives each consumer an option to simply apply its best response strategy to current electric load and tariff in the power distribution system. Using NSGA II optimization technique on load prediction, it is obtained that an area-load based pricing method is beneficial for both electric utility and consumer. Finally, simulation results substantiate that the proposed approach can maximize load factor and reduce total energy cost as well as user's daily electricity charges. View full abstract»

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  • Reduction of Energy Storage Requirements in Future Smart Grid Using Electric Springs

    Page(s): 1282 - 1288
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    The electric spring is an emerging technology proven to be effective in i) stabilizing smart grid with substantial penetration of intermittent renewable energy sources and ii) enabling load demand to follow power generation. The subtle change from output voltage control to input voltage control of a reactive power controller offers the electric spring new features suitable for future smart grid applications. In this project, the effects of such subtle control change are highlighted, and the use of the electric springs in reducing energy storage requirements in power grid is theoretically proven and practically demonstrated in an experimental setup of a 90 kVA power grid. Unlike traditional Statcom and Static Var Compensation technologies, the electric spring offers not only reactive power compensation but also automatic power variation in non-critical loads. Such an advantageous feature enables non-critical loads with embedded electric springs to be adaptive to future power grid. Consequently, the load demand can follow power generation, and the energy buffer and therefore energy storage requirements can be reduced. View full abstract»

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  • Energy Management for Lifetime Extension of Energy Storage System in Micro-Grid Applications

    Page(s): 1289 - 1296
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    Energy storage is needed in micro-grid to help solve the problem of intermittency introduced by renewable energy sources, enhance power quality and improve controllability of power flow. This paper presents an energy manager for energy storage system (ESS) in micro-grids. The objectives of the energy manager are focused on improving the energy efficiency and extending the life expectancy of ESS while ensuring constraints of energy storage modules are complied with. To this end a smart local prediction and local scheduling algorithm is proposed. A battery lifetime model that uses the proposed Peukert lifetime energy throughput based on the workload of the battery is developed. Verification shows that in the long run, the energy manger can improve overall energy efficiency of ESS from 74.1% to 85.5%, and improve estimated lifetime of 2 Battery Packs in ESS from 3.6 years and 2.4 years to 5 years and 5.7 years respectively. View full abstract»

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  • Decentralized Voltage Control to Minimize Distribution Power Loss of Microgrids

    Page(s): 1297 - 1304
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    Microgrids that integrate renewable power sources are suitable for rural communities or certain military applications such as forward operation bases. For microgrids that are not connected to the large electric grid, new control strategies must be designed to maintain proper grid voltage and frequency. In addition, microgrids with distributed power sources and load nodes may have frequent reconfiguration in grid architecture. Therefore, the control strategies ideally should be “plug-and-play”, i.e., they should not require significant communication or architecture information, and they should work reliably as long as the supply/demand powers are reasonably balanced. Another unique issue of microgrids is the high resistance loss in distribution lines due to the low operating voltage. To reduce power losses, appropriate voltage control at distributed nodes is required which again must work in a plug-and-play fashion. In this paper, we propose a decentralized voltage control algorithm that minimizes power losses for microgrids. Its optimality and plug-and-play nature are demonstrated through comprehensive simulations. View full abstract»

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  • Periodic Flexible Demand: Optimization and Phase Management in the Smart Grid

    Page(s): 1305 - 1313
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    Based on measurements obtained by commodity wireless sensors, we observe that the majority of thermostatic loads in a user premise are described by periodic pulse waves. We propose a novel first stage of optimization in the smart grid which reduces external on/off command flow for demand response between the controller and the smart appliances. A phase management scheme is developed that defines optimal time shifts on the periodic loads in order to provide peak power and energy cost reduction over a limited time horizon. A gradient descent optimization technique, based on Taylor series, is applied to determine the phases of the pulses in discrete time steps. Three optimization strategies and two control schemes are explored. Minimization of peak power loads, minimization of energy costs and flattening of the power curve are modeled. A centralized and a distributed control scheme are explored. It is found that respectable peak power and cost reduction can be achieved in the centralized control scheme but redundant data transfer in the network and increased complexity is necessary. On the other hand, the distributed control scheme reduces the overall complexity but does not present significant savings. View full abstract»

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  • An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

    Page(s): 1314 - 1322
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    Wind power is gaining in significance as an important renewable source of clean energy. However, due to their inherent uncertainty, wind generators are often unable to participate in the forward electricity markets like the more predictable and controllable conventional generators. Given this, virtual power plants (VPPs) are being advocated as a solution for increasing the reliability of such intermittent renewable sources. In this paper, we take this idea further by considering VPPs as coalitions of wind generators and electric vehicles, where wind generators seek to use electric vehicles (EVs) as a storage medium to overcome the vagaries of generation. Using electric vehicles in this manner has the advantage that, since the number of EVs is increasing rapidly, no initial investment in dedicated storage is needed. In more detail, we first formally model the VPP and then, through an operational model based on linear programming, we show how the supply to the Grid and storage in the EV batteries can be scheduled to increase the profit of the VPP, while also paying for the storage using a novel scheme. The feasibility of our approach is examined through a realistic case-study, using real wind power generation data, corresponding electricity market prices and electric vehicles' characteristics. View full abstract»

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  • Induction Motor Starting in Islanded Microgrids

    Page(s): 1323 - 1331
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    Motor loads require high amount of reactive power for a short period during their startup. The high reactive power drawn from the system causes voltage dips at startup time and potentially overvoltage after motor startup is over. The voltage dip and overvoltage may cause the relays to trip and the system to go unstable. This phenomenon is more challenging in weak distribution systems and isolated systems such as microgrids due to the limited inertia of the master generator. This paper presents a dynamic voltage controller that coordinates all the reactive power sources in the system to provide the necessary reactive power during motor startup. The presented Model Predictive Control (MPC) based dynamic Volt/Var Control (VVC) scheme considers the dynamics of the microgrid in the VVC formulation to overcome the voltage dip caused by motor startup. This method uses predictions of voltage behavior of the system based on a simplified system model and tries to eliminate the effect of motor startup by coordinating the reactive power sources in the system. View full abstract»

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  • Occupancy Prediction Algorithms for Thermostat Control Systems Using Mobile Devices

    Page(s): 1332 - 1340
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    Several techniques have been proposed for the automatic control of just-in-time heating and cooling systems in indoor spaces that accommodate both the occupants' comfort and energy savings. Current techniques, however, do not provide an adequate solution for efficient thermostat control, because they require costly infrastructures to detect occupancy or because they inaccurately predict the occupancy due to irregular patterns. In this paper, we propose an automatic thermostat control system based on the mobility prediction of users, using contextual information obtained by mobile phones. We also present an arrival time prediction scheme that combines both historical pattern and route classification. The experimental results indicate that the proposed system can successfully predict at least 70% of the transit cases within 10 minutes' error and can decrease energy consumption by 26%. View full abstract»

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  • Decentralized Coordination of Energy Utilization for Residential Households in the Smart Grid

    Page(s): 1341 - 1350
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2104 KB) |  | HTML iconHTML  

    In this paper, we investigate the minimization of the total energy cost of multiple residential households in a smart grid neighborhood sharing a load serving entity. Specifically, each household may have renewable generation, energy storage as well as inelastic and elastic energy loads, and the load serving entity attempts to coordinate the energy consumption of these households in order to minimize the total energy cost within this neighborhood. The renewable generation, the energy demand arrival, and the energy cost function are all stochastic processes and evolve according to some, possibly unknown, probabilistic laws. We develop an online control algorithm, called Lyapunov-based cost minimization algorithm (LCMA), which jointly considers the energy management and demand management decisions. LCMA only needs to keep track of the current values of the underlying stochastic processes without requiring any knowledge of their statistics. Moreover, a decentralized algorithm to implement LCMA is also developed, which can preserve the privacy of individual household owners. Numerical results based on real-world trace data show that our control algorithm can effectively reduce the total energy cost in the neighborhood. View full abstract»

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  • Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems

    Page(s): 1351 - 1360
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (871 KB) |  | HTML iconHTML  

    Despite the economic and environmental advantages of plug-in hybrid electric vehicles (PHEVs), the increased utilization of PHEVs brings up new concerns for power distribution system decision makers. Impacts of PHEVs on distribution networks, although have been proven to be noticeable, have not been thoroughly investigated for future years. In this paper, a comprehensive model is proposed to study the PHEV impacts on residential distribution systems. In so doing, PHEV fundamental characteristics, i.e., PHEV battery capacity, PHEV state of charge (SOC), and PHEV energy consumption in daily trips, are accurately modeled. As some of these effective characteristics depend on vehicle owner's behavior, their behavior and interests are considered in the proposed model. Also, to get a more practical model of PHEVs, the number of vehicles in a residential distribution network, the PHEV penetration level for upcoming years, distribution of PHEVs in the network, and estimation of household load growth for upcoming years are extracted from related published reports. The proposed model is applied to the IEEE 34-node test feeder, and PHEV impacts on residential distribution network are studied in different time horizons. A sensitivity analysis is also performed to demonstrate the effects of PHEV operation modes on the network load profile. View full abstract»

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  • Value of Storage in Distribution Grids—Competition or Cooperation of Stakeholders?

    Page(s): 1361 - 1370
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    The implementation of storage capacities in distribution grids is seen as an important element for the integration of fluctuating feed-in caused by photovoltaic and wind generators. However, the responsibility for the operating of these assets is not defined in most market designs. Since decreasing costs are to be expected with further market penetration, next to distribution grid operators (DSO) further storage stake holders may be interested in controlling local storage devices. In this paper optimal storage profiles for different stakeholders (DSO and energy traders) are derived based on a case study with real world data. The results reveal conflicting interests-peak shaving of fluctuating feed-in (objective o the DSO to avoid reinforcements) is hampered significantly by storage usage of trading companies (objective of exploiting price spreads in the spot market). It is shown that unreasonable high costs occur with undesired economical side-effects if no control or cooperation mechanism is implemented. View full abstract»

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  • Nonintrusive, Self-Organizing, and Probabilistic Classification and Identification of Plugged-In Electric Loads

    Page(s): 1371 - 1380
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2007 KB) |  | HTML iconHTML  

    Electricity consumption for plugged-in electric loads (PELs) accounts for more use than any other single end-use service in residential and commercial buildings. PELs possess potentials to be efficiently managed for many purposes. However, few existing load identification methods are designed for PELs to handle challenges such as the diversity within each type of PELs and similarity between different types of PELs with similar front-end power supply units. Existing methods provide only absolute decisions which are not reliable when handling these challenges. This paper presents a simple yet efficient and practical hybrid supervised self-organizing map (SSOM)/Bayesian identifier for PELs. The proposed identifier can classify PELs into clusters by inherent similarities due to similar front-end power supply topologies, extract and utilize statistical information, and provide the probability of the unknown load belonging to a specific type of load. Tests based on real-world data validate that the proposed methods are accurate, robust, and applicable. View full abstract»

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  • Integrated V2G, G2V, and Renewable Energy Sources Coordination Over a Converged Fiber-Wireless Broadband Access Network

    Page(s): 1381 - 1390
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1704 KB) |  | HTML iconHTML  

    In this paper, an integrated vehicle-to-grid, grid-to-vehicle, and renewable energy sources (IntVGR) coordination algorithm is proposed. The focus of this work is to provide a multidisciplinary study on implementing the proposed IntVGR scheme over a broadband fiber-wireless communications infrastructure by co-simulating both power and communications perspectives. For the power systems perspective, results show that the scheme is able to achieve a 21% reduction in peak demand compared to uncontrolled charging, and a better performance in flattening the overall demand profile and maintaining network constraints in comparison to a benchmark scenario. The scheme has also been demonstrated to successfully coordinate PEVs to take maximum utilization of local renewable energy. For the communications perspective, the measured upstream traffic for executing the proposed IntVGR scheme on a residential area of 342 households is found to be 1-2 Mbps with an end-to-end latency level of 1 ms. The scheme has also been validated from both perspectives in a sensitivity analysis with a higher PEV adoption rate. View full abstract»

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  • An Optimal Power Scheduling Method for Demand Response in Home Energy Management System

    Page(s): 1391 - 1400
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    With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem. View full abstract»

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  • MPC-Based Appliance Scheduling for Residential Building Energy Management Controller

    Page(s): 1401 - 1410
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1575 KB) |  | HTML iconHTML  

    This paper proposes an appliance scheduling scheme for residential building energy management controllers, by taking advantage of the time-varying retail pricing enabled by the two-way communication infrastructure of the smart grid. Finite-horizon scheduling optimization problems are formulated to exploit operational flexibilities of thermal and non-thermal appliances using a model predictive control (MPC) method which incorporates both forecasts and newly updated information. For thermal appliance scheduling, the thermal mass of the building, which serves as thermal storage, is integrated into the optimization problem by modeling the thermodynamics of rooms in a building as constraints. Within the comfort range modeled by the predicted mean vote (PMV) index, thermal appliances are scheduled smartly together with thermal mass storage to hedge against high prices and make use of low-price time periods. For non-thermal appliance scheduling, in which delay and/or power consumption flexibilities are available, operation dependence of inter-appliance and intra-appliance is modeled to further exploit the price variation. Simulation results show that customers have notable energy cost savings on their electricity bills with time-varying pricing. The impact of customers' preferences of appliances usage on energy cost savings is also evaluated. View full abstract»

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  • Profit-Optimal and Stability-Aware Load Curtailment in Smart Grids

    Page(s): 1411 - 1420
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    A key feature of future smart grids is demand response. With the integration of a two-way communication infrastructure, a smart grid allows its operator to monitor the production and usage of power in real time. Upon detection of significant events, the operator may send requests to intelligent loads to curtail their power usage. The operator can use load curtailments reactively for adaptation to the loss of generation capacity (e.g., with unpredictable renewable energy sources), or proactively for profit maximization by avoiding the use of expensive energy sources during peak hours. In this paper, we optimize operator profits for the different cases of load curtailment, under various practical constraints including the physical properties of the power system, and different cost and valuation functions for heterogeneous generation units and loads, respectively. We also investigate the requirements imposed by different cases of the load curtailment on the cyber infrastructure. In particular, we evaluate how the delay of cyber control impacts the frequency stability of the power grid during the load curtailment phase. View full abstract»

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  • Non-Intrusive Signature Extraction for Major Residential Loads

    Page(s): 1421 - 1430
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    This paper presents a technique to extract load signatures non-intrusively by using the smart meter data. Load signature extraction is different from load activity identification. It is a new and important problem to solve for the applications of non-intrusive load monitoring (NILM). For a target appliance whose signatures are to be extracted, the proposed technique first selects the candidate events that are likely to be associated with the appliance by using generic signatures and an event filtration step. It then applies a clustering algorithm to identify the authentic events of this appliance. In the third step, the operation cycles of appliances are estimated using an association algorithm. Finally, the electric signatures are extracted from these operation cycles. The results can have various applications. One is to create signature databases for the NILM applications. Another is for load condition monitoring. Validation results based on the data collected from three actual houses and a laboratory experiment have shown that the proposed method is a promising solution to the problem of load signature collection. View full abstract»

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  • Parallel Load Schedule Optimization With Renewable Distributed Generators in Smart Grids

    Page(s): 1431 - 1441
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    We propose a framework for demand response in smart grids that integrates renewable distributed generators (DGs). In this model, some users have DGs and can generate part of their electricity. They can also sell extra generation to the utility company. The goal is to optimize the load schedule of users to minimize the utility company's cost and user payments, while considering user satisfaction. We employ a parallel autonomous optimization scheme, where each user requires only the knowledge of the aggregated load of other users, instead of the load profiles of individual users. All the users can execute distributed optimization simultaneously. The distributed optimization is coordinated through a soft constraint on changes of load schedules between iterations. Numerical examples show that our method can significantly reduce the peak-hour load and costs to the utility and users. Since the autonomous user optimization is executed in parallel, our method also significantly decreases the computation time and communication costs. View full abstract»

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Aims & Scope

The IEEE Transactions on Smart Grid is a cross disciplinary and internationally archival journal aimed at disseminating results of research on smart grid that relates to, arises from, or deliberately influences energy generation, transmission, distribution and delivery. The journal publishes original research on theories and development on principles of smart grid technologies and systems. The Transactions also welcomes manuscripts on design, implementation and evaluation of power systems that are affected by smart grid. Surveys of existing work on smart grid may also be considered for publication when they propose a new viewpoint on history and a challenging perspective on the future of smart grid.

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Meet Our Editors

Editor-in-Chief
Mohammad Shahidehpour
Illinois Institute of Technology