Computational Methods for Residential Energy Cost Optimization in Smart Grids

Abstract

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.

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

“…In matching buyers to sellers, strategies based on auction theory are commonly used. However, their efficiency can be improved by incorporating a prediction algorithm to forecast energy demand and price in the MG [109]. Furthermore, future game theory applications in prosumer energy trading could involve several types of games such as facility-location games, Stackelberg games, advanced hash games, and others.…”

On-Line Building Energy Optimization Using Deep Reinforcement Learning

“…Understanding such individual consumption behavior based on the knowledge transfer from the fusion of extensive data collected from the Advanced Metering Infrastructure (AMI) is an essential step to optimize building energy consumption and consequently the effects of its use. This work is motivated by the hypothesis that an optimal resource allocation of end-user patterns based on daily smart electrical device profiles could be used to smoothly reconcile differences in future energy consumption patterns and the E. Mocanu supply of variable sources such as wind and solar [1]- [3]. It is expected that a cost minimization problem could be solved to activate real-time price responsive behavior [4].…”

A cyber-physical systems perspective on decentralized energy management

“…Figure 2 in [156]), but also the numerous recent survey publications on smart grids (e.g. [3,10,30,136,156] [51] and [121].…”