The Experts below are selected from a list of 16185 Experts worldwide ranked by ideXlab platform

Massoud Pedram - One of the best experts on this subject based on the ideXlab platform.

  • adaptive control for energy Storage systems in households with photovoltaic modules
    IEEE Transactions on Smart Grid, 2014
    Co-Authors: Yanzhi Wang, Massoud Pedram
    Abstract:

    Integration of residential-level photovoltaic (PV) power generation and energy Storage systems into the smart grid will provide a better way of utilizing renewable power. With dynamic energy pricing models, consumers can use PV-based generation and controllable Storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential Storage Controller should possess the ability of forecasting future PV power generation as well as the power consumption profile of the household for better performance. In this paper, novel PV power generation and load power consumption prediction algorithms are presented, which are specifically designed for a residential Storage Controller. Furthermore, to perform effective Storage control based on these predictions, the proposed Storage control algorithm is separated into two tiers: the global control tier and the local control tier. The former is performed at decision epochs of a billing period (a month) to globally “plan” the future discharging/charging schemes of the Storage system, whereas the latter one is performed more frequently as system operates to dynamically revise the Storage control policy in response to the difference between predicted and actual power generation and consumption profiles. The global tier is formulated and solved as a convex optimization problem at each decision epoch, whereas the local tier is analytically solved. Finally, the optimal size of the energy Storage module is determined so as to minimize the break-even time of the initial investment in the PV and Storage systems.

  • a hierarchical control algorithm for managing electrical energy Storage systems in homes equipped with pv power generation
    Green Technologies Conference, 2012
    Co-Authors: Yanzhi Wang, Siyu Yue, Massoud Pedram, Louis Joseph Kerofsky, Sachin G Deshpande
    Abstract:

    Integrating residential-level photovoltaic (PV) power generation and energy Storage systems into the smart grid will provide a better way of utilizing renewable power. This has become a particularly interesting problem with the availability of dynamic energy pricing models in which electricity consumers can use their PV-based generation and controllable Storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential-level Storage Controller should possess the ability of forecasting future PV-based power generation and load power consumption profiles for better performance. In this paper we present novel PV power generation and load power consumption prediction algorithms, which are specifically designed for a residential Storage Controller. Furthermore, to perform effective Storage control based on these predictions, we separate the proposed Storage control algorithm into two tiers, one which is performed at decision epochs of a billing period (e.g., a month) to globally "plan" the future discharging/charging schemes of the Storage system, and another one performed locally and more frequently as system operates to compensate prediction errors. The first tier of algorithm is formulated and solved as a convex optimization problem at each decision epoch of the billing period, while the second tier has O(1) complexity.

Stamatios Chondrogiannis - One of the best experts on this subject based on the ideXlab platform.

  • Smart grid energy Storage Controller for frequency regulation and peak shaving, using a vanadium redox flow battery
    International Journal of Electrical Power and Energy Systems, 2016
    Co-Authors: Alexandre Lucas, Stamatios Chondrogiannis
    Abstract:

    Grid connected energy Storage systems are regarded as promising solutions for providing ancillary services to electricity networks and to play an important role in the development of smart grids. Thus far, the more mature battery technologies have been installed in pilot projects and studies have indicated their main advantages and shortcomings. The main concerns for wide adoption are the overall cost, the limited number of charging cycles (or lifetime), the depth of discharge, the low energy density and the sustainability of materials used. Vanadium Redox Flow Batteries (VRFB) are a promising option to mitigate many of these shortcomings, and demonstration projects using this technology are being implemented both in Europe and in the USA. This study presents a model using MATLAB/Simulink, to demonstrate how a VRFB based Storage device can provide multi-ancillary services, focusing on frequency regulation and peak-shaving functions. The study presents a Storage system at a medium voltage substation and considers a small grid load profile, originating from a residential neighbourhood and fast charging stations demand. The model also includes an inverter Controller that provides a net power output from the battery system, in order to offer both services simultaneously. Simulation results show that the VRFB Storage device can regulate frequency effectively due to its fast response time, while still performing peak-shaving services. VRFB potential in grid connected systems is discussed to increase awareness of decision makers, while identifying the main challenges for wider implementation of Storage systems, particularly related to market structure and standardisation requirements.

Yanzhi Wang - One of the best experts on this subject based on the ideXlab platform.

  • adaptive control for energy Storage systems in households with photovoltaic modules
    IEEE Transactions on Smart Grid, 2014
    Co-Authors: Yanzhi Wang, Massoud Pedram
    Abstract:

    Integration of residential-level photovoltaic (PV) power generation and energy Storage systems into the smart grid will provide a better way of utilizing renewable power. With dynamic energy pricing models, consumers can use PV-based generation and controllable Storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential Storage Controller should possess the ability of forecasting future PV power generation as well as the power consumption profile of the household for better performance. In this paper, novel PV power generation and load power consumption prediction algorithms are presented, which are specifically designed for a residential Storage Controller. Furthermore, to perform effective Storage control based on these predictions, the proposed Storage control algorithm is separated into two tiers: the global control tier and the local control tier. The former is performed at decision epochs of a billing period (a month) to globally “plan” the future discharging/charging schemes of the Storage system, whereas the latter one is performed more frequently as system operates to dynamically revise the Storage control policy in response to the difference between predicted and actual power generation and consumption profiles. The global tier is formulated and solved as a convex optimization problem at each decision epoch, whereas the local tier is analytically solved. Finally, the optimal size of the energy Storage module is determined so as to minimize the break-even time of the initial investment in the PV and Storage systems.

  • a hierarchical control algorithm for managing electrical energy Storage systems in homes equipped with pv power generation
    Green Technologies Conference, 2012
    Co-Authors: Yanzhi Wang, Siyu Yue, Massoud Pedram, Louis Joseph Kerofsky, Sachin G Deshpande
    Abstract:

    Integrating residential-level photovoltaic (PV) power generation and energy Storage systems into the smart grid will provide a better way of utilizing renewable power. This has become a particularly interesting problem with the availability of dynamic energy pricing models in which electricity consumers can use their PV-based generation and controllable Storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential-level Storage Controller should possess the ability of forecasting future PV-based power generation and load power consumption profiles for better performance. In this paper we present novel PV power generation and load power consumption prediction algorithms, which are specifically designed for a residential Storage Controller. Furthermore, to perform effective Storage control based on these predictions, we separate the proposed Storage control algorithm into two tiers, one which is performed at decision epochs of a billing period (e.g., a month) to globally "plan" the future discharging/charging schemes of the Storage system, and another one performed locally and more frequently as system operates to compensate prediction errors. The first tier of algorithm is formulated and solved as a convex optimization problem at each decision epoch of the billing period, while the second tier has O(1) complexity.

Alexandre Lucas - One of the best experts on this subject based on the ideXlab platform.

  • Smart grid energy Storage Controller for frequency regulation and peak shaving, using a vanadium redox flow battery
    International Journal of Electrical Power and Energy Systems, 2016
    Co-Authors: Alexandre Lucas, Stamatios Chondrogiannis
    Abstract:

    Grid connected energy Storage systems are regarded as promising solutions for providing ancillary services to electricity networks and to play an important role in the development of smart grids. Thus far, the more mature battery technologies have been installed in pilot projects and studies have indicated their main advantages and shortcomings. The main concerns for wide adoption are the overall cost, the limited number of charging cycles (or lifetime), the depth of discharge, the low energy density and the sustainability of materials used. Vanadium Redox Flow Batteries (VRFB) are a promising option to mitigate many of these shortcomings, and demonstration projects using this technology are being implemented both in Europe and in the USA. This study presents a model using MATLAB/Simulink, to demonstrate how a VRFB based Storage device can provide multi-ancillary services, focusing on frequency regulation and peak-shaving functions. The study presents a Storage system at a medium voltage substation and considers a small grid load profile, originating from a residential neighbourhood and fast charging stations demand. The model also includes an inverter Controller that provides a net power output from the battery system, in order to offer both services simultaneously. Simulation results show that the VRFB Storage device can regulate frequency effectively due to its fast response time, while still performing peak-shaving services. VRFB potential in grid connected systems is discussed to increase awareness of decision makers, while identifying the main challenges for wider implementation of Storage systems, particularly related to market structure and standardisation requirements.

Benami Yassour - One of the best experts on this subject based on the ideXlab platform.

  • adding advanced Storage Controller functionality via low overhead virtualization
    File and Storage Technologies, 2012
    Co-Authors: Muli Benyehuda, Michael Factor, Avishay Traeger, Eran Borovik, Benami Yassour
    Abstract:

    Historically, Storage Controllers have been extended by integrating new code, e.g., file serving, database processing, deduplication, etc., into an existing base. This integration leads to complexity, co-dependency and instability of both the original and new functions. Hypervisors are a known mechanism to isolate different functions. However, to enable extending a Storage Controller by providing new functions in a virtual machine (VM), the virtualization overhead must be negligible, which is not the case in a straightforward implementation. This paper demonstrates a set of mechanisms and techniques that achieve near zero runtime performance overhead for using virtualization in the context of a Storage system.