7,646 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
Charge Scheduling of an Energy Storage System under Time-of-use Pricing and a Demand Charge
A real-coded genetic algorithm is used to schedule the charging of an energy
storage system (ESS), operated in tandem with renewable power by an electricity
consumer who is subject to time-of-use pricing and a demand charge. Simulations
based on load and generation profiles of typical residential customers show
that an ESS scheduled by our algorithm can reduce electricity costs by
approximately 17%, compared to a system without an ESS, and by 8% compared to a
scheduling algorithm based on net power.Comment: 13 pages, 2 figures, 5 table
Smart Microgrids: Overview and Outlook
The idea of changing our energy system from a hierarchical design into a set
of nearly independent microgrids becomes feasible with the availability of
small renewable energy generators. The smart microgrid concept comes with
several challenges in research and engineering targeting load balancing,
pricing, consumer integration and home automation. In this paper we first
provide an overview on these challenges and present approaches that target the
problems identified. While there exist promising algorithms for the particular
field, we see a missing integration which specifically targets smart
microgrids. Therefore, we propose an architecture that integrates the presented
approaches and defines interfaces between the identified components such as
generators, storage, smart and \dq{dumb} devices.Comment: presented at the GI Informatik 2012, Braunschweig Germany, Smart Grid
Worksho
A Distributed Demand-Side Management Framework for the Smart Grid
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand
Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids
In this paper, we propose and study the effectiveness of customer engagement
plans that clearly specify the amount of intervention in customer's load
settings by the grid operator for peak load reduction. We suggest two different
types of plans, including Constant Deviation Plans (CDPs) and Proportional
Deviation Plans (PDPs). We define an adjustable reference temperature for both
CDPs and PDPs to limit the output temperature of each thermostat load and to
control the number of devices eligible to participate in Demand Response
Program (DRP). We model thermostat loads as power throttling devices and design
algorithms to evaluate the impact of power throttling states and plan
parameters on peak load reduction. Based on the simulation results, we
recommend PDPs to the customers of a residential community with variable
thermostat set point preferences, while CDPs are suitable for customers with
similar thermostat set point preferences. If thermostat loads have multiple
power throttling states, customer engagement plans with less temperature
deviations from thermostat set points are recommended. Contrary to classical
ON/OFF control, higher temperature deviations are required to achieve similar
amount of peak load reduction. Several other interesting tradeoffs and useful
guidelines for designing mutually beneficial incentives for both the grid
operator and customers can also be identified
Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids
Electric vehicle fleets and smart grids are two growing technologies. These technologies
provided new possibilities to reduce pollution and increase energy efficiency.
In this sense, electric vehicles are used as mobile loads in the power grid. A distributed
charging prioritization methodology is proposed in this paper. The solution is based
on the concept of virtual power plants and the usage of evolutionary computation
algorithms. Additionally, the comparison of several evolutionary algorithms, genetic
algorithm, genetic algorithm with evolution control, particle swarm optimization, and
hybrid solution are shown in order to evaluate the proposed architecture. The proposed
solution is presented to prevent the overload of the power grid
Optimized Household Demand Management with Local Solar PV Generation
Demand Side Management (DSM) strategies are of-ten associated with the
objectives of smoothing the load curve and reducing peak load. Although the
future of demand side manage-ment is technically dependent on remote and
automatic control of residential loads, the end-users play a significant role
by shifting the use of appliances to the off-peak hours when they are exposed
to Day-ahead market price. This paper proposes an optimum so-lution to the
problem of scheduling of household demand side management in the presence of PV
generation under a set of tech-nical constraints such as dynamic electricity
pricing and voltage deviation. The proposed solution is implemented based on
the Clonal Selection Algorithm (CSA). This solution is evaluated through a set
of scenarios and simulation results show that the proposed approach results in
the reduction of electricity bills and the import of energy from the grid
Smart home energy management
The new challenges on Information and Communication Technologies (ICT) in Automatic Home
Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management
flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or
an outdoor space, equipped with sensors and actuators to collect data and send controls according to
the activities and expectations of the occupants/users. Home automation provides a centralized or
distributed control of electrical appliances. Adding intelligence to the home environment, it would
be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and
outside the dwelling, for instance using smart solutions for the management of the external lights and
of the garden
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