コンピュータ・サイエンス : ACM computing surveys

Peer-to-Peer energy trading in a Microgrid

The electricity industry is now at the verge of a new era—an era that promises, through the evolution of the existing electrical grids to smart grids, more efficient and effective power management, better reliability, reduced production costs, and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around not only the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the smart grid and the smart home, which we present as an integral part of the smart grid. Based on several scenarios, we aim to present some of the most representative threats to the smart home/smart grid environment. The threats detected are categorized according to specific security goals set for the smart home/smart grid environment, and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes this paper.

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Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems.

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Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model

Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge. *Corresponding author at: Department of Electronics and Communication Engineering, Tenaga Nasional Universiti, P13-B-07-06, Sri Cempaka, Jalan Sepakat Indah 2/2 Taman Sep, Kajang 43000 Selangor, Malaysia. Tel.: +60 183262643.

A review of residential demand response of smart grid

A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home.

A Review of Smart Homes—Past, Present, and Future

Peer-to-peer energy trading among smart homes

This research addresses a demand side management (DSM) system coordinated with Peer-to-Peer (P2P) energy trading among the households in the smart grid It considers the components which have significant impact on cost optimization, eg, storage, renewables, and microgrid The model utilizes load and source scheduling, and energy trading strategies for cost optimization It also addresses the inconvenience created to the users by delaying certain tasks The contributions of the research are threefold First, to our knowledge, this is the first optimal model which integrates DSM with P2P energy trading The solutions of the proposed model determine optimal microgrid energy and price for P2P trading, which was not considered previously Second, P2P energy trading in the microgrid potentially results in an unfair cost distribution among the participating households We address this unfair cost distribution problem by employing Pareto optimality, ensuring that no households will be worse off to improve the cost of others Third, our proposed trading strategy considers total cost optimization in a microgrid The model utilizes all available energy to minimize energy cost Therefore, there is a very low risk of energy waste, which is typically neglected in other energy trading strategies

An optimal P2P energy trading model for smart homes in the smart grid

This paper proposes an algorithm, called sequence prediction via enhanced episode discovery (SPEED), to predict inhabitant activity in smart homes. SPEED is a variant of the sequence prediction algorithm. It works with the episodes of smart home events that have been extracted based on the on -off states of home appliances. An episode is a set of sequential user activities that periodically occur in smart homes. The extracted episodes are processed and arranged in a finite-order Markov model. A method based on prediction by partial matching (PPM) algorithm is applied to predict the next activity from the previous history. The result shows that SPEED achieves an 88.3% prediction accuracy, which is better than LeZi Update, Active LeZi, IPAM, and C4.5.

SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes

A smart power grid transforms the traditional electric grid into a user-centric, intelligent power network. The cost-saving potential of smart homes is an excellent motivating factor to involve users in smart grid operations. To that end, this survey explores the contemporary cost-saving strategies for smart grids from the users’ perspective. The study shows that optimization methods are the most popular cost-saving techniques reported in the literature. These methods are used to plan scheduling and power utilization schemes of household appliances, energy storages, renewables, and other energy generation devices. The survey shows that trading energy among neighborhoods is one of the effective methods for cost optimization. It also identifies the prediction methods that are used to forecast energy price, generation, and consumption profiles, which are required to optimize energy cost in advance. The contributions of this article are threefold. First, it discusses the computational methods reported in the literature with their significance and limitations. Second, it identifies the components and their characteristics that may reduce energy cost. Finally, it proposes a unified cost optimization framework and addresses the challenges that may influence the overall residential energy cost optimization problem in smart grids.

Muhammad Raisul Alam

Papers

A Review of Smart Homes—Past, Present, and Future

Peer-to-peer energy trading among smart homes

An optimal P2P energy trading model for smart homes in the smart grid

SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes

Computational Methods for Residential Energy Cost Optimization in Smart Grids: A Survey