9,399 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Efficient energy management for the internet of things in smart cities
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities
Emission-aware Energy Storage Scheduling for a Greener Grid
Reducing our reliance on carbon-intensive energy sources is vital for
reducing the carbon footprint of the electric grid. Although the grid is seeing
increasing deployments of clean, renewable sources of energy, a significant
portion of the grid demand is still met using traditional carbon-intensive
energy sources. In this paper, we study the problem of using energy storage
deployed in the grid to reduce the grid's carbon emissions. While energy
storage has previously been used for grid optimizations such as peak shaving
and smoothing intermittent sources, our insight is to use distributed storage
to enable utilities to reduce their reliance on their less efficient and most
carbon-intensive power plants and thereby reduce their overall emission
footprint. We formulate the problem of emission-aware scheduling of distributed
energy storage as an optimization problem, and use a robust optimization
approach that is well-suited for handling the uncertainty in load predictions,
especially in the presence of intermittent renewables such as solar and wind.
We evaluate our approach using a state of the art neural network load
forecasting technique and real load traces from a distribution grid with 1,341
homes. Our results show a reduction of >0.5 million kg in annual carbon
emissions -- equivalent to a drop of 23.3% in our electric grid emissions.Comment: 11 pages, 7 figure, This paper will appear in the Proceedings of the
ACM International Conference on Future Energy Systems (e-Energy 20) June
2020, Australi
Simplified Algorithm for Dynamic Demand Response in Smart Homes Under Smart Grid Environment
Under Smart Grid environment, the consumers may respond to incentive--based
smart energy tariffs for a particular consumption pattern. Demand Response (DR)
is a portfolio of signaling schemes from the utility to the consumers for load
shifting/shedding with a given deadline. The signaling schemes include
Time--of--Use (ToU) pricing, Maximum Demand Limit (MDL) signals etc. This paper
proposes a DR algorithm which schedules the operation of home appliances/loads
through a minimization problem. The category of loads and their operational
timings in a day have been considered as the operational parameters of the
system. These operational parameters determine the dynamic priority of a load,
which is an intermediate step of this algorithm. The ToU pricing, MDL signals,
and the dynamic priority of loads are the constraints in this formulated
minimization problem, which yields an optimal schedule of operation for each
participating load within the consumer provided duration. The objective is to
flatten the daily load curve of a smart home by distributing the operation of
its appliances in possible low--price intervals without violating the MDL
constraint. This proposed algorithm is simulated in MATLAB environment against
various test cases. The obtained results are plotted to depict significant
monetary savings and flattened load curves.Comment: This paper was accepted and presented in 2019 IEEE PES GTD Grand
International Conference and Exposition Asia (GTD Asia). Furthermore, the
conference proceedings has been published in IEEE Xplor
Decentralized Demand Side Management with Rooftop PV in Residential Distribution Network
In the past extensive researches have been conducted on demand side
management (DSM) program which aims at reducing peak loads and saving
electricity cost. In this paper, we propose a framework to study decentralized
household demand side management in a residential distribution network which
consists of multiple smart homes with schedulable electrical appliances and
some rooftop photovoltaic generation units. Each smart home makes individual
appliance scheduling to optimize the electric energy cost according to the
day-ahead forecast of electricity prices and its willingness for convenience
sacrifice. Using the developed simulation model, we examine the performance of
decentralized household DSM and study their impacts on the distribution network
operation and renewable integration, in terms of utilization efficiency of
rooftop PV generation, overall voltage deviation, real power loss, and possible
reverse power flows.Comment: 5 pages, 7 figures, ISGT 2018 conferenc
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