Abstract
The concept of transactive energy (TE) in smart grid systems is gaining increased research attention for its potential to optimize distributed energy resources, improve system reliability, as well as provide a balanced ecosystem for fair economic transaction between prosumers. TE is defined by the GridWise Architecture Council as a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter. With control mechanisms being a key part of TE systems, in this article, we discuss the state-of-the-art in TE control strategies, architectures, and relevant simulators for designing, evaluating, and analysing TE systems. Most importantly, existing TE control strategies are examined and discussed via a hierarchical structure comprising four different levels wherein TE control strategies/controllers can be deployed. Architecture-wise, we highlight the different types of TE architectures including the centralized, decentralized, distributed, and hierarchical architecture. In terms of existing and potential simulators for designing and evaluating TE models, we discuss and compare notable software across different characteristics of interest. We conclude this article by highlighting the basic components of a typical TE controller and other future research directions spanning across security concerns, privacy issues, communication challenges, simulation and validation demands. As a main contribution, different from existing survey articles, this article presents a synthesis of existing works regarding TE control strategies, architectures, and TE-based simulators for the benefit of the budding researcher whose interest may lie in the study of TE systems.
Highlights
Since the early 2000s, many private and public electric utilities around the world have been steadily migrating from traditional power grid systems to the more enterprising concept of smart grid (SG) networks [1]
Many assumptions are being made in the modelling phase of transactive energy (TE) systems, which may in turn pose significant problems under reallife deployment conditions
The authors concluded that the simulated MG would lose its resilience to cyberattacks assuming the actual components in the loop where considered in the study and they would retain their resilience if the physical components were not considered
Summary
Since the early 2000s, many private and public electric utilities around the world have been steadily migrating from traditional power grid systems to the more enterprising concept of smart grid (SG) networks [1]. SG networks are composed of many independent and geographically distributed power generation sites that are capable of being integrated into the main grid. Such independent and distributed power generation sites are typically referred to as microgrids (MGs). MGs comprise several distributed energy resources (DER) and loads such as solar panels, wind turbines, combined heat and power generators, electric vehicle (EV) charging stations, and energy storage facilities like batteries. These energy sources are responsible for generating and storing power locally within an MG. Spatial-wise, MGs may serve discrete geographical footprints, such as college
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