Web services for indoor energy management in a smart grid environment

Khan, Adnan Afsar; Mouftah, Hussein T.

doi:10.1109/pimrc.2011.6139654

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…The energy management of microgrid can perform real-time energy forecasting of renewable resources, energy storage, elements, and controllable loads in making proper shortterm scheduling to minimize total operating costs. Cooperation among different microgrids in a smart microgrid In [34], it is stated that the poor service life of batteries is one of the major issues that hinder the development of standalone microgrid. Hybrid energy storage systems (HESS) and novel power management strategies have been proposed by researchers to address this issue and to improve the battery cycles.…”

Section: Dsm In Nanogrid Microgrid and Smart Gridmentioning

confidence: 99%

Variable Load Demand Scheme for Hybrid AC/DC Nanogrid

Rauf

Kalair

Khan

2020

International Journal of Photoenergy

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This paper addresses the use of nanogrid technology in resolving the issue of blanket load shedding for domestic consumers. This is accomplished by using different load management techniques and load classification and utilizing maximum solar energy. The inclusion of DC-based load in basic load and DC inverter load in regular load and scheduling of the burst load during the hours of maximum solar PV generation bring novelty in this work. The term “nanogrid” as a power structure remains ambiguous in various publications so far. An effort has been done in this paper to present a concise definition of nanogrid. Demand side load management is one of the key features of nanogrid, which enables end users to know major characteristics about their energy consumption during peak and off-peak hours. A microgrid option with nanogrid facility results in a more reliable system with overall improvement in efficiency and reduction in carbon emission. PV plants produce DC power; when used directly, the loss will automatically be minimized to 16%. The AC/DC hybrid nanogrid exhibits 63% more efficiency as compared to AC-only nanogrid and nearly 18% more efficiency as compared to DC-only nanogrid. Smart load shifting smoothens the demand curve 54% more adequately than during conventional load shifting. Simulation results show that real-time pricing is more economical than flat rate tariff for a house without DG, whereas flat rate results are more economical when DG are involved in nanogrids. 12.67%-21.46% saving is achieved if only flat rates are used for DG in nanogrid instead of real-time pricing.

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Section: Dsm In Nanogrid Microgrid and Smart Gridmentioning

confidence: 99%

Variable Load Demand Scheme for Hybrid AC/DC Nanogrid

Rauf

Kalair

Khan

2020

International Journal of Photoenergy

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“…In addition, there is no mention of the usage of self-produced energy, which, in turn, could change the attitude toward price elasticity (buying a small quantity of additional energy at a high price while most of it is self-produced). Web services are the key components to interact in a smart-grid-enabled home, as suggested in [4] and [5]. These works, however, are mostly an exercise in Web service interaction and simulation of Web service communication (representing appliances and sensors) rather than a real set of services or components that can be applied in a smart-grid test solution.…”

Section: Related Workmentioning

confidence: 99%

“…The current practice to run simulation has mostly been to use few static predetermined tariff prices, thus having variable prices although not dynamic [4], [5]. In this paper, we design and realize a tool that is able to generate real-time prices based on actual power system conditions, allowing for more realistic simulations and more precise results.…”

Section: Introductionmentioning

confidence: 99%

Generating Realistic Dynamic Prices and Services for the Smart Grid

Pagani

Aiello

2015

IEEE Systems Journal

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The smart grid promises to change the way people manage their energy needs, to facilitate the inclusion of small-scale renewable sources, and to open the energy market to all. One of the enabling instruments is the real-time pricing of energy at the retail level: dynamic and flexible tariffs will vary through the day to reflect the actual availability of energy and the congestion conditions of the power grid, in turn, helping the power grid to stay in balance. Current pilots and research efforts consider how such dynamic tariffs can be formed and how these will affect energy distribution and usage, although currently there are no instances of them deployed. To experiment with dynamic pricing, we present a vision and related implementation of the services needed by the end user to enable the smart grid in a smart home. Our system realistically simulates the dynamic prices and services of the smart grid, using data coming from wholesale energy markets and renewable installations. In addition, we perform an economic analysis to assess the cost of energy produced by small renewable-based installations. The aim is to offer a testing tool to easily realize large simulations, testbeds, and pilot projects for future energy distribution scenarios. We have tested the proposed services and dynamic pricing solution in an existing living laboratory, showing the feasibility of the approach and effectiveness of the tool.

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“…Although this architecture is valuable and enables a good simulation in the large of the smart grid, it lacks in considering the prosumer interactions that are a key ingredient for the future smart grid; for this purpose, we consider an agent-based system better suited. Web services are the key components envisioned in the smart grid as suggested in [2,25]. However, these works are mainly a test related to Web service interaction and simulation of Web services communication for instance representing appliances and sensors.…”

Section: Related Workmentioning

confidence: 99%

An adaptive agent-based system for deregulated smart grids

Capodieci

Pagani

Cabri

et al. 2015

SOCA

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The power grid is undergoing a major change due mainly to the increased penetration of renewables and novel digital instruments in the hands of the end users that help to monitor and shift their loads. Such transformation is only possible with the coupling of an information and communication technology infrastructure to the existing power distribution grid. Given the scale and the interoperability requirements of such future system, service-oriented architectures (SOAs) are seen as one of the reference models and are considered already in many of the proposed standards for the smart grid (e.g., IEC-62325 and OASIS eMIX). Beyond the technical issues of what the service-oriented architectures of the smart grid will look like, there is a pressing question about what the added value for the end user could be. Clearly, the operators need to guarantee availability and security of supply, but why should the end users care? In this paper, we explore a scenario in which the end users can both consume and produce small quantities of energy and can trade these quantities in an open and deregulated market. For the trading, they delegate software agents that can fully interoperate and interact with one another thus taking advantage of the SOA. In particular, the agents have strategies, inspired from game theory, to take advantage of a service-oriented smart grid market and give profit to their delegators, while implicitly helping balancing the power grid. The proposal is implemented with simulated agents and interaction with existing Web services. To show the advantage of the agent with strategies, we compare our approach with the “base” agent one by means of simulations, highlighting the advantages of the proposal

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Web services for indoor energy management in a smart grid environment

Cited by 17 publications

References 9 publications

Variable Load Demand Scheme for Hybrid AC/DC Nanogrid

Variable Load Demand Scheme for Hybrid AC/DC Nanogrid

Generating Realistic Dynamic Prices and Services for the Smart Grid

An adaptive agent-based system for deregulated smart grids

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