Abstract
The smart grid is generally studied as an efficient and powerful electric grid. With the assistance of information and communication technology (ICT), the electric grid can increase the performance of the power grid system with smart energy management. On the other hand, with the usage of renewable energy resources (RERs), smart energy storage, and new transmission technologies in the power grid system, various new features such as real-time monitoring, fast restoration, battery displays, automated outage management, etc. have been assimilated into the smart grid. These new features generate more complexity in energy transmission and constitute important challenges like low energy consumption, high energy cost, social welfare, etc. while designing energy trading mechanisms in the smart grid. In the Internet-of-Things (IoT) era, several scenarios such as micro-grids, energy harvesting networks, and vehicle-to-grid (V2G) networks are present where energy trading plays an important role. However, in these scenarios, there are energy transmission and distribution, security and privacy, energy consumption, system reliability, the criticality of data delivery, and a few more challenges caused by distrust, non-transparent, and uncertain energy markets. Motivated from these challenges, we present a four-layered architecture of energy trading used in the smart grid. We propose a comprehensive background regarding the main concepts of energy trading and the implication of enabling technologies that manage the energy imbalances in the smart grid. Then, we present a problem taxonomy based on incentive, mathematical, and simulation model-driven approaches, which are widely used to control and maintain the energy trading mechanisms. Based on the findings from the literature, we also present a solution taxonomy with enabling technologies such as Energy Internet, Software-defined networking (SDN), and blockchain. In the end, a summary of future research directions based on the energy trading mechanisms is explored to provide deep insights to the readers.
Highlights
Internet-of-Things (IoT) is an important part of smart grid to improve the power grid system by giving timely and efficient information and communication to the stakeholders [1]
3) This paper describes a solution taxonomy based on enabling technologies like Software-defined networking (SDN), Energy Internet, and blockchain to improve the energy cost and energy consumption in the smart grid effectively and efficiently
From the facts discussed in mathematical models, we found that optimization techniques used in energy trading mechanisms are highly useful
Summary
Internet-of-Things (IoT) is an important part of smart grid to improve the power grid system by giving timely and efficient information and communication to the stakeholders [1]. Among all of these mechanisms, energy trading is one of the most effective mechanisms, which accounts for the concern of both the supply and the demand sides In this mechanism, the prosumers aim to provide electricity to consumers and adhere to the physical constraints of an electric grid [4]. The participation of prosumers and consumers in the wholesale market is accepted as the inevitable solution to enhance the economic efficiency of energy markets, reduce peak demand and price volatility, and improve the reliability of electric power systems. The DR aggregation is acknowledged as an efficient solution to increasing the exposure of large volumes of consumers to wholesale energy markets In this way, DR aggregators work with the customers to offer appropriate DR programs that would allow customers to participate in the wholesale energy market. Optimization, linear programming, Markov decision process (MDP), reinforcement learning, etc. are used, which improve the energy consumption and find the right behavior of energy trading participants
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