The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Niangjun Chen - One of the best experts on this subject based on the ideXlab platform.

  • data center demand response avoiding the coincident peak via workload shifting and Local Generation
    Performance Evaluation, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable Generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone.

  • data center demand response avoiding the coincident peak via workload shifting and Local Generation
    Measurement and Modeling of Computer Systems, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

  • SIGMETRICS - Data center demand response: avoiding the coincident peak via workload shifting and Local Generation
    Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems - SIGMETRICS '13, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

  • Data center demand response: Avoiding the coincident peak via workload shifting and Local Generation
    Performance Evaluation, 2013
    Co-Authors: Zhenhua Liu, Benjamin Razon, Adam Wierman, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable Generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone. © 2013 Elsevier B.V. All rights reserved.

Stephane Grieu - One of the best experts on this subject based on the ideXlab platform.

  • A rule-based strategy to the predictive management of a grid-connected residential building in southern France
    Sustainable Cities and Society, 2017
    Co-Authors: Aurélie Chabaud, Julien Eynard, Stephane Grieu
    Abstract:

    In this paper is presented a rule-based strategy to the predictive management of the energy resources in a residential building one can equip with Power generators (i.e. photovoltaic solar panels and a vertical-axis wind turbine) and batteries for electricity storage. The strategy takes the status of the electricity grid into consideration (via grid thresholds) and aims at both favouring self-consumption of the electricity produced from renewables and minimizing the negative impact of Local Power Generation on the grid operation. It is based on anticipating the total-occupant load of the building, the grid load as well as the variable Power that comes from renewables, using a rolling forecast horizon. Note that we have previously proposed a non-predictive strategy and pointed out possible improvements, in particular regarding the management of the batteries (Chabaud et al., 2015). So, a grid-connected residential building located in Perpignan (southern France) has been modelled using the TRNSYS software. Performance has been evaluated thanks to energy and economic criteria. Taking a look at the results we have obtained in simulation, one can highlight configurations that offer a good compromise between self-consumption of electricity and the renewable energy coverage rate. The combination of photovoltaic solar panels and a vertical-axis wind turbine has been highlighted as a viable energy mix option for residential buildings in southern France. Clearly, optimally designing and managing the Power generators and batteries added to the building using the predictive strategy improve the way that building and the electricity grid interact. In particular, the batteries are better handled, allowing electricity to be injected to the grid and extracted from the grid at more favourable times.

  • A new approach to energy resources management in a grid-connected building equipped with energy production and storage systems: A case study in the south of France
    Energy and Buildings, 2015
    Co-Authors: Aurélie Chabaud, Julien Eynard, Stephane Grieu
    Abstract:

    In the present paper, a new approach to energy resources management in a residential microgrid is proposed and evaluated in simulation, using energy and economic criteria. Its aim is to improve energy efficiency as well as interaction with the electricity grid. So, a grid-connected building located in Perpignan (south of France) and equipped with energy production and storage systems has been modelled using the TRNSYS software. We designed and managed these systems optimally and highlighted configurations that promote self-consumption of energy. In addition, the negative impact on the grid of Local Power Generation (related to both energy injection and extraction) is minimized. The combination of photovoltaic solar panels and a vertical-axis wind turbine as a viable energy mix option for residential buildings in Southern France has also been evaluated. At least, we appraised the way electricity storage impacts on performance as well as the savings one can achieve by shifting some domestic loads from on-peak to off-peak periods.

Adam Wierman - One of the best experts on this subject based on the ideXlab platform.

  • data center demand response avoiding the coincident peak via workload shifting and Local Generation
    Performance Evaluation, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable Generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone.

  • data center demand response avoiding the coincident peak via workload shifting and Local Generation
    Measurement and Modeling of Computer Systems, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

  • SIGMETRICS - Data center demand response: avoiding the coincident peak via workload shifting and Local Generation
    Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems - SIGMETRICS '13, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

  • Data center demand response: Avoiding the coincident peak via workload shifting and Local Generation
    Performance Evaluation, 2013
    Co-Authors: Zhenhua Liu, Benjamin Razon, Adam Wierman, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable Generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone. © 2013 Elsevier B.V. All rights reserved.

Anne Remke - One of the best experts on this subject based on the ideXlab platform.

  • MASCOTS - Energy Storage in Smart Homes: Grid-Convenience Versus Self-Use and Survivability
    2016 IEEE 24th International Symposium on Modeling Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 2016
    Co-Authors: Jannik Huels, Anne Remke
    Abstract:

    The number of Local Power Generation units, such as photovoltaic panels (PV), increased enormously in recent years. Their production patterns are highly variable and depend on the current weather. The resulting fluctuation in production poses a major challenge to the stability of the Power grid. The use of Local energy storages may help to ensure that the Locally produced Power is fed into the grid in a grid-convenient way. It may also help clients to increase the self-use of Locally generated Power and to increase the so-called survivability of their homes in the presence of a Power outage. This paper compares the interest of the Power operator, i.e. grid-convenience with the interests of the user, i.e. self-use and survivability for different battery management strategies: i) direct loading, ii) delayed loading and iii) peak shaving. We use a Hybrid Petri Net model with one stochastic variable (HPnG) to model smart homes with Local Power Generation, Local storage and different battery management strategies in the presence of Power outages. Recent algorithms for analyzing and model checking HPnGs enable the computation of the above mentioned measures of interest. We are able to show that whenever good predictions of production and demand exist, grid-convenience does not decrease the survivability and the self-use of a smart home.

  • Energy Storage in Smart Homes: Grid-Convenience Versus Self-Use and Survivability
    2016 IEEE 24th International Symposium on Modeling Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), 2016
    Co-Authors: Jannik Huels, Anne Remke
    Abstract:

    The number of Local Power Generation units, such as photovoltaic panels (PV), increased enormously in recent years. Their production patterns are highly variable and depend on the current weather. The resulting fluctuation in production poses a major challenge to the stability of the Power grid. The use of Local energy storages may help to ensure that the Locally produced Power is fed into the grid in a grid-convenient way. It may also help clients to increase the self-use of Locally generated Power and to increase the so-called survivability of their homes in the presence of a Power outage. This paper compares the interest of the Power operator, i.e. grid-convenience with the interests of the user, i.e. self-use and survivability for different battery management strategies: i) direct loading, ii) delayed loading and iii) peak shaving. We use a Hybrid Petri Net model with one stochastic variable (HPnG) to model smart homes with Local Power Generation, Local storage and different battery management strategies in the presence of Power outages. Recent algorithms for analyzing and model checking HPnGs enable the computation of the above mentioned measures of interest. We are able to show that whenever good predictions of production and demand exist, grid-convenience does not decrease the survivability and the self-use of a smart home.

Benjamin Razon - One of the best experts on this subject based on the ideXlab platform.

  • data center demand response avoiding the coincident peak via workload shifting and Local Generation
    Performance Evaluation, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable Generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone.

  • data center demand response avoiding the coincident peak via workload shifting and Local Generation
    Measurement and Modeling of Computer Systems, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

  • SIGMETRICS - Data center demand response: avoiding the coincident peak via workload shifting and Local Generation
    Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems - SIGMETRICS '13, 2013
    Co-Authors: Adam Wierman, Benjamin Razon, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

  • Data center demand response: Avoiding the coincident peak via workload shifting and Local Generation
    Performance Evaluation, 2013
    Co-Authors: Zhenhua Liu, Benjamin Razon, Adam Wierman, Yuan Chen, Niangjun Chen
    Abstract:

    Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given their high and increasing energy consumption and their flexibility in demand management compared to conventional industrial facilities. In this paper, we study two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of Local Power Generation. We conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and Local Power Generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern, workload demand and renewable Generation prediction error distributions. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with Local Generation can provide significant cost savings (up to 40% under the Fort Collins Utilities charging scheme) compared to either alone. © 2013 Elsevier B.V. All rights reserved.