Annual Energy Outlook 2008 With Projections to 2030

This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer�s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user�s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer�s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller to minimize the household�s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks.

Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints

Electric power monthly

The fusion of Internet of Things (IoTs) and computational intelligence makes it possible to increase energetic efficiency of our homes. Connected devices can be optimally adjusted to the needs of a family. In this article, we present our developed IoT convection installation for a small house with the developed remote platform control system. The control module is gartering readings from sensors and information from users about conditions in the house and, by the use of computational intelligence, optimizes parameters to adjust the developed IoT convection system for better comfort of a family. We have done a full convection installation, both in practical and theoretical models, together with remote control system and the proposed security model. Optimization results show increased comfort of use with lower changes in the temperature inside. The system after optimization shows significant improvement in lower changes of the temperature and lower consumption.

Intelligent Internet of Things System for Smart Home Optimal Convection

Considering both non-line-of-sight (NLoS) and line-of-sight (LoS) transmissions, the transitional behaviors from noise-limited regime to dense interference-limited regime have been investigated for the fifth generation (5G) small cell networks (SCNs). Besides, we identify four performance regimes based on base station (BS) density, i.e., 1) the noise-limited regime, 2) the signal-dominated regime, 3) the interference-dominated regime, and 4) the interference-limited regime . To characterize the performance regime, we propose a unified framework analyzing the future 5G wireless networks over generalized shadowing/fading channels, in which the user association schemes based on the strongest instantaneous received power and the strongest average received power can be studied, while NLoS/LoS transmissions and multislop path loss model are considered. Simulation results indicate that different factors, i.e., noise, desired signal, and interference, successively and separately dominate the network performance with the increase of BS density. Hence, our results shed new light on the design and management of SCNs in urban and rural areas with different BS deployment densities.

Dense Small Cell Networks: From Noise-Limited to Dense Interference-Limited

Energy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: 1) a Reinforcement Learning-based algorithm, which provides a bounded regret and 2) a more computationally efficient heuristic, named BPT- ${K}$ , with guaranteed termination and correctness. A comprehensive experimental analysis is conducted against state-of-the-art solutions using realistic datasets. Results show that including user modeling and learning provides significant performance improvements compared to state-of-the-art approaches. Specifically, the proposed algorithms result in 25% higher efficiency and 27% more transferred energy. Furthermore, the learning algorithms converge to a value less than 5% of the optimal solution in less than 3 months of learning.

A Reinforcement Learning Approach for User Preference-Aware Energy Sharing Systems

In this paper, we study the Energy Efficiency (EE) and Area Spectral Efficiency (ASE) of green random cellular networks which utilize the Cell Zooming (CZ) techniques. To this aim, using stochastic geometry, we derive a tractable expression for the Ergodic Capacity in a Poisson Voronoi Tessellation (PVT) random cellular network in which both Base Station (BS) and Mobile User (MU) locations are drawn randomly from two independent Poisson Point Processes (PPPs). The performance of this network is examined under different MU densities and two CZ algorithms. Numerical evaluations show that there is an optimum transmission power, which maximizes EE in PVT random cellular networks. On the other hand, increasing the transmission power and the cell size does not improve ASE much more after passing a threshold. The tradeoff between EE and ASE is also presented.

Energy and Area Spectral Efficiency of Cell Zooming in Random Cellular Networks

In this paper, we study the performance of ${K}$ –tier heterogeneous random cellular networks (HetNets) in terms of energy efficiency (EE) and spectral efficiency (SE). In order to improve efficiency, two cell zooming (CZ) techniques, namely telescopic and ON/OFF schemes, are applied to these networks. Accordingly, we investigate two scenarios in this paper. First, we focus on a single tier scenario and derive a new expression for the ergodic capacity of a Poisson–Voronoi tessellation random cellular network using stochastic geometry. Besides, the EE and SE of this model are depicted for several user densities and CZ parameters. In the second scenario, we investigate multitier HetNets in which the base stations gather their required energy from the environment. Therefore, two important concepts in green cellular networks, namely CZ and energy harvesting, are addressed in this paper. Subsequently, numerical results show the performance improvement in terms of the coverage and blocking probabilities in addition to EE and SE.

Energy Harvesting and Cell Zooming in $K-$ Tier Heterogeneous Random Cellular Networks

Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances are contingent on the use of others. We define a perceived-value user utility used as an Integer Linear Programming (ILP) problem. We show that the problem is NP-Hard and provide a heuristic method called COndensed DependencY (CODY). We validate our results using synthetic and real datasets, large-scale online experiments, and a real-field experiment at the Missouri University of Science and Technology Solar Village. Simulation results show that our approach achieves near optimal performance and significantly outperforms previously proposed solutions. Results from our online and real-field experiments also show that users largely prefer our solution compared to a previous approach.

Perceived-Value-driven Optimization of Energy Consumption in Smart Homes

Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a Mixed Integer Linear Programming (MILP) problem, and show that the problem is NPHard. Since the solution of such problem requires the knowledge of the user behavioral model, we propose an heuristic based on reinforcement learning with bounded regret to learn such model while optimizing the system performance. Comparison with the state-of-the-art approaches using realistic simulations based on real traces shows that our method outperforms existing schemes in several efficiency metrics. Besides, the results reveal that increasing the amount of produced energy improves the learning ability of the system even in a short period. It gives practical insights for implementation of energy sharing systems.

Atieh R. Khamesi

Papers

A Reinforcement Learning Approach for User Preference-Aware Energy Sharing Systems

Energy and Area Spectral Efficiency of Cell Zooming in Random Cellular Networks

Energy Harvesting and Cell Zooming in $K-$ Tier Heterogeneous Random Cellular Networks

Perceived-Value-driven Optimization of Energy Consumption in Smart Homes

Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning.