The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Takahiro Shiga - One of the best experts on this subject based on the ideXlab platform.

  • online mechanisms for charging electric vehicles in settings with varying marginal Electricity Costs
    International Conference on Artificial Intelligence, 2015
    Co-Authors: Keiichiro Hayakawa, Enrico H Gerding, Sebastian Stein, Takahiro Shiga
    Abstract:

    We propose new mechanisms that can be used by a demand response aggregator to flexibly shift the charging of electric vehicles (EVs) to times where cheap but intermittent renewable energy is in high supply. Here, it is important to consider the constraints and preferences of EV owners, while eliminating the scope for strategic behaviour. To achieve this, we propose, for the first time, a generic class of incentive mechanisms for settings with both varying marginal Electricity Costs and multi-dimensional preferences. We show these are dominant strategy incentive compatible, i.e., EV owners are incentivised to report their constraints and preferences truthfully. We also detail a specific instance of this class, show that it achieves ≈ 98% of the optimal in realistic scenarios and demonstrate how it can be adapted to trade off efficiency with profit.

  • IJCAI - Online mechanisms for charging electric vehicles in settings with varying marginal Electricity Costs
    2015
    Co-Authors: Keiichiro Hayakawa, Enrico H Gerding, Sebastian Stein, Takahiro Shiga
    Abstract:

    We propose new mechanisms that can be used by a demand response aggregator to flexibly shift the charging of electric vehicles (EVs) to times where cheap but intermittent renewable energy is in high supply. Here, it is important to consider the constraints and preferences of EV owners, while eliminating the scope for strategic behaviour. To achieve this, we propose, for the first time, a generic class of incentive mechanisms for settings with both varying marginal Electricity Costs and multi-dimensional preferences. We show these are dominant strategy incentive compatible, i.e., EV owners are incentivised to report their constraints and preferences truthfully. We also detail a specific instance of this class, show that it achieves ≈ 98% of the optimal in realistic scenarios and demonstrate how it can be adapted to trade off efficiency with profit.

Jarmo Partanen - One of the best experts on this subject based on the ideXlab platform.

  • ISGT Europe - Electric vehicle smart charging aims for CO 2 emission reduction
    2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2016
    Co-Authors: Ville Tikka, Jukka Lassila, Juha Haakana, Jarmo Partanen
    Abstract:

    In this paper, a methodology for electric vehicle charging by minimizing CO 2 emissions and minimizing charging Electricity Costs is presented in an actual Nordic Electricity market environment. The target of the paper is to illustrate the difference of smart charging and dumb charging schemes from the perspectives of CO 2 emissions and Electricity end-user Electricity Costs. The study takes advantage of a national transportation survey and actual Electricity market data including generation-type-specific CO 2 information.

  • Electric vehicle smart charging aims for CO2 emission reduction?
    2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2016
    Co-Authors: Ville Tikka, Jukka Lassila, Juha Haakana, Jarmo Partanen
    Abstract:

    In this paper, a methodology for electric vehicle charging by minimizing CO2 emissions and minimizing charging Electricity Costs is presented in an actual Nordic Electricity market environment. The target of the paper is to illustrate the difference of smart charging and dumb charging schemes from the perspectives of CO2 emissions and Electricity end-user Electricity Costs. The study takes advantage of a national transportation survey and actual Electricity market data including generation-type-specific CO2 information.

Prashant Shenoy - One of the best experts on this subject based on the ideXlab platform.

  • BuildSys@SenSys - Minimizing Electricity Costs by sharing energy in sustainable microgrids
    2014
    Co-Authors: Zhichuan Huang, Aditya Mishra, David Irwin, Yu Gu, Prashant Shenoy
    Abstract:

    Buildings account for over 75% of the Electricity consumption in the United States. To reduce Electricity usage and peak demand, many utilities are introducing market-based time-of-use (TOU) pricing models. In parallel, government programs that increase the fraction of renewable energy are incentivizing residential consumers to adopt on-site renewables and energy storage. Connecting on-site renewables and energy storage between homes forms a sustainable microgrid capable of generating, storing, and sharing Electricity to balance local generation and consumption in residential areas. In this paper, we investigate how to minimize the Costs of Electricity from a utility for a microgrid under market-based TOU pricing models. In particular, we (i) present a system architecture for an energy-sharing microgrid; and (ii) develop optimal energy-sharing algorithms for homes within the microgrid. We conduct an extensive evaluation under two typical TOU pricing models that use data from more than 40 homes. Our results indicate that our system reduces the Costs of Alternating Current (AC) Electricity by 20%, even for homes with similar energy usage patterns.

  • Minimizing Electricity Costs by sharing energy in sustainable microgrids
    Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings - BuildSys '14, 2014
    Co-Authors: Zhichuan Huang, Ting Zhu, Aditya Mishra, David Irwin, Yu Gu, Prashant Shenoy
    Abstract:

    Buildings account for over 75% of the Electricity consumption in the United States. To reduce Electricity usage and peak demand, many utilities are introducing market-based time-of-use (TOU) pricing models. In parallel, government programs that increase the fraction of renewable energy are incentivizing residential consumers to adopt on-site renewables and energy storage. Connecting on-site renewables and energy storage between homes forms a sustainable microgrid capable of generating, storing, and sharing Electricity to balance local generation and consumption in residential areas. In this paper, we investigate how to minimize the Costs of Electricity from a utility for a microgrid under market-based TOU pricing models. In particular, we (i) present a system architecture for an energy-sharing microgrid; and (ii) develop optimal energy-sharing algorithms for homes within the microgrid. We conduct an extensive evaluation under two typical TOU pricing models that use data from more than 40 homes. Our results indicate that our system reduces the Costs of Alternating Current (AC) Electricity by 20%, even for homes with similar energy usage patterns. Copyright 2014 ACM.

Ammar Rayes - One of the best experts on this subject based on the ideXlab platform.

  • Peak Power Shaving for Reduced Electricity Costs in Cloud Data Centers: Opportunities and Challenges
    IEEE Network, 2020
    Co-Authors: Mehiar Dabbagh, Bechir Hamdaoui, Ammar Rayes
    Abstract:

    An Electricity bill of a data center (DC) is determined not only by how much energy the DC consumes, but especially by how the consumed energy is spread over time during the billing cycle. More specifically, these Electricity Costs are essentially made up of two major charges: Energy Charge, a cost based on the amount of consumed energy (in kWh), and Peak Charge, a cost based on the maximum power (in kW) requested during the billing cycle. The latter charge component is forced to encourage DCs to balance and regulate their power demands over the billing cycle, allowing the utility company to manage congestion without increasing supply. This billing model has thus called for the development of peak power shaving approaches that reduce Costs by smoothing peak power demands over the billing cycle to minimize the Peak Charge component. In this paper, we investigate peak power shaving approaches, and begin by using Google data traces to quantify and provide a real sense of how much Electricity cost reduction can peak power demand shaving achieve on a Google DC cluster. We then discuss why peak power shaving is well-suited for reducing Electricity Costs of DCs, and describe two commonly used peak shaving approaches, namely energy storage and workload modulation. We finally identify and describe key research problems that remain unsolved and require further investigation.

  • Peak Power Shaving for Reduced Electricity Costs in Cloud Data Centers: Opportunities and Challenges
    IEEE Network, 1
    Co-Authors: Mehiar Dabbagh, Bechir Hamdaoui, Ammar Rayes
    Abstract:

    An Electricity bill of a data center (DC) is determined not only by how much energy the DC consumes, but especially by how the consumed energy is spread over time during the billing cycle. More specifically, these Electricity Costs are essentially made up of two major charges: the Energy Charge, a cost based on the amount of consumed energy (in kWh), and a Peak Charge, a cost based on the maximum power (in kW) requested during the billing cycle. The latter charge component is forced to encourage DCs to balance and regulate their power demands over the billing cycle, allowing the utility company to manage congestion without increasing supply. This billing model has thus called for the development of peak power shaving approaches that reduce Costs by smoothing peak power demands over the billing cycle to minimize the Peak Charge component. In this paper, we investigate peak power shaving approaches, and begin by using Google data traces to quantify and provide a real sense of how much Electricity cost reduction can peak power demand shaving achieve on a Google DC cluster. We then discuss why peak power shaving is well-suited for reducing Electricity Costs of DCs, and describe two commonly used peak shaving approaches, namely energy storage and workload modulation. We finally identify and describe key research problems that remain unsolved and require further investigation.

Fathi Abugchem - One of the best experts on this subject based on the ideXlab platform.

  • heuristic optimization of consumer Electricity Costs using a generic cost model
    Energies, 2015
    Co-Authors: Chris Ogwumike, Michael Short, Fathi Abugchem
    Abstract:

    Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy Costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable Costs are negligible.