The penetration of renewable energy in power systems is continually increasing as the global energy system transitions to low-carbon, leading to greater uncertainty intensifying the clearing pressure in electricity markets and the dispatching pressure in power systems. These factors pose challenges to the selection of power-balancing mechanisms. This paper focuses on the typical decentralized decision-making balancing mechanism exemplified by the Balancing Group mechanism in Germany. It analyzes its advantages, such as enhanced autonomy of market participants, reduced aggregation of system uncertainty, and lowered dispatch pressure. The paper first introduces the mechanism's fundamental structure and operational methods, highlighting the role of balancing groups as a key component. Next, it analyzes the characteristics of the German Balancing Group mechanism rules and the supporting mechanisms in actual operations and explores the decentralized nature of this mechanism. Finally, it explores the issues and adaptability faced in introducing related market-oriented balancing mechanisms in China, offering recommendations on dispatch levels, market participation, market integration, and technical support.

Equilibrium analysis has been widely studied as an effective tool to model gaming interactions and predict market results. However, as competition modes are fundamentally changed by the decarbonization and decentralization of power systems, analysis techniques must evolve. This article comprehensively reviews recent developments in modelling methods, practical settings and solution techniques in equilibrium analysis. Firstly, we review equilibrium in the evolving wholesale power markets which feature new entrants, novel trading products and multi-stage clearing. Secondly, the competition modes in the emerging distribution market and distributed resource aggregation are reviewed, and we compare peer-to-peer clearing, cooperative games and Stackelberg games. Furthermore, we summarize the methods to treat various information acquisition degrees, risk preferences and rationalities of market participants. To deal with increasingly complex market settings, this review also covers refined analytical techniques and agent-based models used to compute the equilibrium. Finally, based on this review, this paper summarizes key issues in the gaming and equilibrium analysis in power markets under decarbonization and decentralization.

Dynamic Security Assessment (DSA) for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources (RES) and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, the increasing complexity and the severe computational bottleneck in real-time operation encourage researchers to exploit machine learning to extract offline security rules for the online assessment. However, traditional machine learning methods lack in providing information on the confidence of their corresponding predictions. A better understanding of confidence of the prediction is of key importance for Transmission System Operators (TSOs) to use and rely on these machine learning methods. Specifically, from the perspective of topological changes, it is often unclear whether the machine learning model can still be used. Hence, being aware of the confidence of the prediction supports the transition to using machine learning in real-time operation. In this paper, we propose a novel Conditional Bayesian Deep Auto-Encoder (CBDAC) based security assessment framework to compute a confidence metric of the prediction. This informs not only the operator to judge whether the prediction can be trusted, but it also allows for judging whether the model needs updating. A case study based on IEEE 68-bus system demonstrates that CBDAC outperforms the state-of-the-art machine learning-based DSA methods and the models that need updating under different topologies can be effectively identified. Furthermore, the case study verifies that effective updating of the models is possible even with very limited data.

Following the formulation of certain strategic policy goals, such as reducing greenhouse gases (GHGs) and including more renewable sources (RES) as part of the energy mix in several parts of the world, the practical translation and actual implementation of these goals have led to the introduction of substantial volumes of intermittent renewable electric sources. Because affordable bulk storage for electricity is still lacking, demand and supply need to be (instantaneously) balanced. The resulting challenge that intermittent renewable power sources pose to the controllability of the electric power system requires greater flexibility from other parts of the system, as well as flexibility through interaction with other energy sectors such as the heating sector, the natural gas sector, and the transportation sector.