8,090 research outputs found
A Novel Multiobjective Optimization Algorithm for Home Energy Management System in Smart Grid
Demand response (DR) is an effective method to lower peak-to-average ratio of demand, facilitate the integration of renewable resources (e.g., wind and solar) and plug-in hybrid electric vehicles, and strengthen the reliability of power system. In smart grid, implementing DR through home energy management system (HEMS) in residential sector has a great significance. However, an algorithm that only optimally controls parts of HEMS rather than the overall system cannot obtain the best results. In addition, single objective optimization algorithm that minimizes electricity cost cannot quantify user’s comfort level and cannot take a tradeoff between electricity cost and comfort level conveniently. To tackle these problems, this paper proposes a framework of HEMS that consists of grid, load, renewable resource (i.e., solar resource), and battery. In this framework, a user has the ability to sell electricity to utility grid for revenue. Different comfort level indicators are proposed for different home appliances according to their characteristics and user preferences. Based on these comfort level indicators, this paper proposes a multiobjective optimization algorithm for HEMS that minimizes electricity cost and maximizes user’s comfort level simultaneously. Simulation results indicate that the algorithm can reduce user’s electricity cost significantly, ensure user’s comfort level, and take a tradeoff between the cost and comfort level conveniently
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 Optimization for Smart Grid and Smart Community
The electric power industry has undergone significant changes in response to the environmental concerns during the past decades. Nowadays, due to the integration of different distributed energy systems in the smart grid, the balancing between power generation and load demand becomes a critical problem. Specifically, due to the intermittent nature of renewable energy sources (RESs) , power system optimization becomes significantly complicated. Due to the uncertain nature of RESs, the system may fail to ensure the power quality which may cause increased operating costs for committing costly reserve units or penalty costs for curtailing load demands. This dissertation presents three projects to study the optimization and control for smart grid and smart community. First, optimal operation of battery energy storage system (BESS) in grid-connected microgrid is studied. Near optimal operation/allocation of the BESS is investigated with the consideration of battery lifetime characteristics. Approximate dynamic programming (ADP) is proposed to solve optimal control policy for time-dependent and finite-horizon BESS problems and performance comparison is done with classical dynamic programming approach. The results show that the ADP can optimize the system operation under different scenarios to maximize the total system revenue. Second, optimal operation of the BESS in islanded microgrid is also studied. Specifically, a new islanded microgrid model is formulated based on Markov decision process. A computationally efficient ADP approach is proposed to solve this energy optimization problem, and achieve near minimum operational cost efficiently. Simulation results show that the proposed ADP can achieve 100% and at least 98% of optimality for deterministic and stochastic case studies, respectively. The performance of the proposed ADP approach also achieved 18:69 times faster response than that of the traditional DP approach for 0:5 million of data samples. Third, a demand side management technique is proposed for the optimization of residential demands with financial incentives. A new design of comfort indicator is proposed considering both thermal and other electric appliances based on consumers’ comfort level. The proposed approach is compared with two existing demand response approaches for both 10-houses and 100-houses simulation studies. For both cases, the proposed approach outperformed the existing approaches in terms of reward incentives and comfort levels
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
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
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