• A novel QoS-enabled load-taliem-ir

    A novel QoS-enabled load scheduling algorithm based on reinforcement learning in software-defned energy internet

    تومان

    Recently, smart grid and Energy Internet (EI) are proposed to solve energy crisis and global warming, where improved communication mechanisms are important. Softwaredefined networking (SDN) has been used in smart grid for realtime monitoring and communicating, which requires steady web-environment with no packet loss and less time delay. With the explosion of network scales, the idea of  multiple controllers has been proposed, where the problem of load scheduling needs to be solved. However, some traditional load scheduling algorithms have inferior robustness under the complicated environments in smart grid, and inferior time efficiency without pre-strategy, which are hard to meet the requirement of smart grid. Therefore, we present a novel controller mind (CM) framework to implement automatic management among multiple controllers. Specially, in order to solve the problem of complexity and pre-strategy in the system, we propose a novel Quality of Service (QoS) enabled load scheduling algorithm based on reinforcement learning in this paper. Simulation results show the effectiveness of our proposed scheme in the aspects of load variation and time efficiency.

  • A novel QoS-enabled load -taliem-ir

    A novel QoS-enabled load scheduling algorithm based on reinforcement learning in software-defned energy internet

    تومان

    Recently, smart grid and Energy Internet (EI) are proposed to solve energy crisis and global warming,  here improved communication mechanisms are important. Softwaredefined networking (SDN) has been used in smart grid for realtime monitoring and communicating, which requires steady web-environment with no packet loss and less time delay. With the explosion of network scales, the idea of multiple  ontrollers has been proposed, where the problem of load scheduling needs to be solved. However,  ome traditional load scheduling algorithms have inferior robustness under the  omplicated environments in smart grid, and inferior time efficiency without pre-strategy, which are hard to meet the requirement of smart grid. Therefore, we present a novel controller mind (CM) framework to implement automatic  anagement among multiple controllers. Specially, in order to solve the problem of complexity and pre- trategy in the system, we propose a novel Quality of Service (QoS) enabled load scheduling algorithm based on reinforcement learning in this paper. Simulation results show the effectiveness of .our proposed scheme in the .aspects of load variation and time efficiency

  • A Secure Cloud Computing Based Framework[taliem.ir]

    A Secure Cloud Computing Based Framework for Big Data Information Management of Smart Grid

    تومان

    Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud  computing based framework for big data information management in smart grids, which we call “Smart-Frame.” The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based  encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

  • Big Data A Survey[taliem.ir]

    Big Data: A Survey

    تومان

    In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background,  discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a  discussion of open problems and future directions.

  • Classification of Future Electricity Market Prices[taliem.ir]

    Classification of Future Electricity Market Prices

    تومان

    Forecasting short-term electricity market prices has been the focus of several studies in recent years.  Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize specific price thresholds as the basis for making short-term scheduling  decisions. In this paper, classification of future electricity market prices with respect to prespecified price  thresholds is introduced. Two alternative models based on support vector machines are proposed in a multi-class, multi-step-ahead price classification context. Numerical results are provided for classifying prices in  Ontario’s and Alberta’s markets.

  • bannertaliem-taliem-ir

    Consumers’ Price Elasticity of Demand Modeling With Economic Effects on Electricity Markets Using an Agent-Based Model

    تومان

    Automated Metering Infrastructure (AMI) is a technology that would allow consumers to exhibit price  elasticity of demand under smart-grid environments. The market power of the generation and transmission companies can be mitigated when consumers respond to price signals. Such responses by consumers can also result in reductions in price spikes, consumer energy bills, and emissions of greenhouse gases and other pollutants. In this paper, we use the Electricity Market Complex Adaptive System (EMCAS), an agent-based model that simulates restructured electricity markets, to explore the impact of consumers’ price elasticity of demand on the performance of the electricity market. An 11-node test network with eight generation  companies and five aggregated consumers is simulated for a period of one month. Results are provided and discussed for a case study based on the Korean power system.

  • Digital Grid Communicative Electrical Grids[taliem.ir]

    Digital Grid: Communicative Electrical Grids of the Future

    تومان

    To support a high penetration of intermittent solar and wind power generation, many regions are planning to add new high capacity transmission lines. These additional transmission lines strengthen grid synchronization, but will also increase the grid’s short circuit capacity, and furthermore will be very costly. With a highly  interconnected grid and variable renewable generation, a small grid failure can easily start cascading outages, resulting in large scale blackout. We introduce the “digital grid,” where large synchronous grids are divided into smaller segmented grids which are connected asynchronously, via multileg IP addressed ac/dc/ac  converters called digital grid routers. These routers communicate with each other and send power among the segmented grids through existing transmission lines, which have been repurposed as digital grid  transmission lines. The digital grid can accept high penetrations of renewable power, prevent cascading  outages, accommodate identifiable tagged electricity flows, record those transactions, and trade electricity as a commodity.

  • Exploiting Home Automation Protocols[taliem.ir]

    Exploiting Home Automation Protocols for Load Monitoring in Smart Buildings

    تومان

    Monitoring and controlling electrical loads is crucial for demand-side energy management in smart grids. Home automation (HA) protocols, such as X10 and Insteon, have provided programmatic load control for  many years, and are being widely deployed in early smart grid field trials. While HA protocols include basic monitoring functions, extreme bandwidth limitations (<180bps) have prevented their use in load monitoring. In this paper, we highlight challenges in designing AutoMeter, a system for exploiting HA for accurate load monitoring at scale. We quantify Insteon’s limitations to query device status—once every 10 seconds to achieve less than 5% loss rate—and then evaluate techniques to disaggregate coarse HA data from fine- grained building-wide power data. In particular, our techniques learn switched load power using on-off-dim events, and tag fine-grained building-wide power data using readings from plug meters every 5 minutes.

  • Exploiting Home Automation Protocols[taliem.ir]

    Exploiting Home Automation Protocols for Load Monitoring in Smart Buildings

    تومان

    Monitoring and controlling electrical loads is crucial for demand-side energy management in smart grids.  Home automation (HA) protocols, such as X10 and Insteon, have provided programmatic load control for many years, and are being widely deployed in early smart grid field trials. While HA protocols include basic  monitoring functions, extreme bandwidth limitations (<180bps) have prevented their use in load monitoring. In this paper, we highlight challenges in designing AutoMeter, a system for exploiting HA for accurate load monitoring at scale. We quantify Insteon’s limitations to query device status—once every 10 seconds to achieve less than 5% loss rate—and then evaluate techniques to disaggregate coarse HA data from fine- grained building-wide power data. In particular, our techniques learn switched load power using on-off-dim events, and tag fine-grained building-wide power data using readings from plug meters every 5 minutes .

  • Integration of Asset Management and Smart Grid[taliem.ir]

    Integration of Asset Management and Smart Grid with Intelligent Grid Management System

    تومان

    Electric power transmission and distribution (T and D) systems are composed of a great deal of aged apparatus, which may cause a decrease in reliability owing to their deterioration. In order to maintain high efficiency and high quality in T and D systems, the authors have proposed an “intelligent grid management system” (IGMS), which determines the optimum maintenance strategy and optimum power flow control based on condition monitoring and diagnostic results of the operating power apparatus. This means that the IGMS essentially includes both concepts of an asset management system and a smart grid. Further, the IGMS optimizes power flow routes and maintenance plans based on the failure risk, T and D loss, overload  operation, life estimation of the power apparatus, customer outage, and other metrics. The impact of  individual apparatus failure affects the entire T and D system’s performance, causing blackouts and  secondary failures. Reduction in reliability of whole system is highly dependent on ageing of the materials. The IGMS evaluates all of the events occurring in the T & D system as the cost. Additionally, the risks are  evaluated in the cost according to the impact of the failure rate estimated by the condition monitoring  diagnosis results of the power apparatus. Insulation system determines the transition in reliability and maintenance cost of the system. In this paper, the IGMS is applied to T and D system models including aged apparatus, such as transformers and circuit breakers, and suitable power flow routes and maintenance  strategies are derived. Consequently, by the effective application of the IGMS, the system reliability can achieve an optimum state, and the total cost can be minimized.

  • Integration of Asset Management and Smart Grid[taliem.ir]

    Integration of Asset Management and Smart Grid with Intelligent Grid Management System

    تومان

    Electric power transmission and distribution (T and D) systems are composed of a great deal of aged  apparatus, which may cause a decrease in reliability owing to their deterioration. In order to maintain high efficiency and high quality in T and D systems, the authors have proposed an “intelligent grid management system” (IGMS), which determines the optimum maintenance strategy and optimum power flow control based on condition monitoring and diagnostic results of the operating power apparatus. This means that the IGMS essentially includes both concepts of an asset management system and a smart grid. Further, the IGMS optimizes power flow routes and maintenance plans based on the failure risk, T and D loss, overload  operation, life estimation of the power apparatus, customer outage, and other metrics. The impact of individual apparatus failure affects the entire T and D system’s performance, causing blackouts and  secondary failures. Reduction in reliability of whole system is highly dependent on ageing of the materials. The IGMS evaluates all of the events occurring in the T & D system as the cost. Additionally, the risks are  evaluated in the cost according to the impact of the failure rate estimated by the condition monitoring  diagnosis results of the power apparatus. Insulation system determines the transition in reliability and maintenance cost of the system. In this paper, the IGMS is applied to T and D system models including aged apparatus, such as transformers and circuit breakers, and suitable power flow routes and maintenance  strategies are derived. Consequently, by the effective application of the IGMS, the system reliability can achieve an optimum state, and the total cost can be minimized.

  • Optimal planning and scheduling of energy hub in presence of wind[taliem.ir]

    Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty

    تومان

    Energy Hub (EH) approach streamlines interconnection of heterogeneous energy infrastructures. The insight facilitates integration of Renewable Energy Resources (RERs) to the infrastructures. Consisting of different technologies, EH satisfies the hub output demands through transferring, converting, or storing the hub input energy carriers. Overall performance of power system depends upon optimal implementation of individual  EHs. In this paper, a mathematical formulation is presented for optimal planning of a developed EH  considering operation constraints. Two Objective Functions (OFs) are represented for deterministic and  stochastic circumstances of wind power, electricity price, and the hub electricity demand. The OFs include  costs associated with the hub investment, operation, reliability, and emission. The EH is constructed by Transformer (T), Combined Heat and Power (CHP), Boiler (B), and Thermal Storage (TS). The EH is  developed by Wind Turbine (WT), Energy Storage (ES), and Demand Response programs (DR). The hub input energy carriers are electricity, gas, and water. The hub output demands are electricity, heat, gas, and water. CPLEX solver of GAMS is employed to solve Mixed Integer Linear Programming (MILP) model of the developed hub. A Monte Carlo simulation is used to generate scenarios trees for the wind, price, and demand. SCENRED tool and Backward/Forward technique of GAMS reduce scenarios to best ten scenarios. Simulation results demonstrate what technology with what capacity should be installed in the EH. The results  substantiate when min/max capacities of the hub technologies are required to be installed in the hub. In the meantime, the results manifest when, what technology, and how much energy carrier should be operated to minimize the costs pertained to the hub investment, operation, reliability, and emission. Effectiveness of WT, ES, and DR in the deterministic and stochastic circumstances and influence of uncertainties of the wind, price, and demand are assessed on the hub planning. Finally, effect of gas network capacity and CHP is evaluated on the hub planning.

  • Smart Dispatch of Generation Resources for[taliem.ir]

    Smart Dispatch of Generation Resources for Restructured Electric Power Systems

    تومان

    This paper addresses the vision of a new generation dispatch system for restructured power systems. As  distributed generation, demand response and renewable energy resources ecome significant portions of  overall system installed capacity, a better system dispatch tool for system control centers is required in order to cope with the increasing amount of uncertainties being introduced by the new resources. A Smart ispatch (SD) framework for regional transmission organizations and transmission system operators to manage large power grids is proposed. In particular, the ability of the new dispatch system to provide a better holistic and forward-looking view of systemconditions and generation patterns will be discussed in detail. Such features are deemed critical for the success of efficient system operations in the future .

  • Unit commitment by dynamic programming for[taliem.ir]

    Unit commitment by dynamic programming for microgrid operational planning optimization and emission reduction

    تومان

    This paper presents a 24 hour ahead microgrid power planning using the approach of unit commitment by dynamic programming. The studied system comprises twelve PVbased active generators with embedded storage and three micro gas turbines. Based on the prediction of the energy available from the PV generator, the storage availability, the micro turbine emission characteristics and the load prediction, a central energy management system calculates a 24-hour ahead plan of the power references for three micro gas turbines and the active generators in order to minimize the CO2 equivalent emissions of the gas turbines.